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WO1992002896A1 - Systeme de commande de processus par reseau neuronal modulaire a configuration en langage naturel - Google Patents

Systeme de commande de processus par reseau neuronal modulaire a configuration en langage naturel Download PDF

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Publication number
WO1992002896A1
WO1992002896A1 PCT/US1991/005258 US9105258W WO9202896A1 WO 1992002896 A1 WO1992002896 A1 WO 1992002896A1 US 9105258 W US9105258 W US 9105258W WO 9202896 A1 WO9202896 A1 WO 9202896A1
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WIPO (PCT)
Prior art keywords
neural network
module
data
computer
training
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PCT/US1991/005258
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English (en)
Inventor
Richard D. Skeirik
Original Assignee
E.I. Du Pont De Nemours And Company
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Application filed by E.I. Du Pont De Nemours And Company filed Critical E.I. Du Pont De Nemours And Company
Publication of WO1992002896A1 publication Critical patent/WO1992002896A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/042—Knowledge-based neural networks; Logical representations of neural networks

Definitions

  • the present invention relates generally to monitoring and control of manufacturing processes, particularly chemical processes, and more specifically, to neural networks used in process control of such processes.
  • SUBSTITUTE SHEET rigorous standards provide for quality assurance in production, installation, final inspection, and testing. They also provide guidelines for quality assurance between a supplier and customer. These standards are expected to become an effective requirement for participation in the EC (European Community) after the removal of trade barriers in 1992.
  • the quality of a manufactured product is a combination of all of the properties of the product which affect its usefulness to its user.
  • Process control is the collection of methods used to produce the best possible product properties in a manufacturing process. Process control is very important in the manufacture of products.
  • Improper process control can result in a product which is totally useless to the user, or in a product which has a lower value to the user.
  • the manufacturer suffers (1) by paying the cost of manufacturing useless products, (2) by losing the opportunity to profitably make a product during that time, and (3) by lost revenue from reduced selling price of poor products.
  • the effectiveness of the process control used by a manufacturer can determine whether the manufacturer's business survives or fails.
  • FIG. 19 shows, in block diagram form, key concepts concerning products made in a manufacturing process.
  • raw materials 1222 are processed under (controlled) process conditions 1906 in a process 1212 to produce a product 1216 having product properties 1904.
  • Examples of raw materials 1222, process conditions 1906, and product properties 1904 are shown in Figure 19. It should be understood that these are merely examples for purposes of illustration.
  • FIG 20 shows a more detailed block diagram of the various aspects of the manufacturing of products 1216 using process 1212.
  • product 1216 is defined by one or more product property aim value(s) 2006 of its product properties 1904.
  • the product property aim values 2006 of the product properties 1904 are those which the product 1216 needs to have in order for it to be ideal for its intended end use.
  • the objective in running process 1212 is to manufacture products 1216 having product properties 1904 which are exactly at the product property aim value(s) 2006.
  • the following simple example of a process 1212 is presented merely for purposes of illustration.
  • the example process 1212 is the baking of a cake.
  • Raw materials 1222 such as flour, milk, baking powder, lemon flavoring, etc.
  • Examples of the (controlled) process conditions 1906 are: mix batter until uniform, bake batter in a pan at a preset oven temperature for a preset time, remove baked cake from pan, and allow removed cake to cool to room temperature.
  • the product 1216 produced in this example is a cake having desired properties 1904. For example, these desired product properties
  • 1904 can be a cake that is fully cooked but not burned, brown on the outside, yellow on the inside, having a suitable lemon flavoring, etc.
  • Process conditions 1906 can be, for example, the properties of the raw materials 1222, the speed at which process 1212 runs (also called the production rate of the process 1212), the process conditions 1906 in each step or stage of the process 1212 (such as temperature, pressure, etc.), the duration of each step or stage, and so on.
  • Figure 20 shows a more detailed block diagram of the various aspects of the manufacturing of products 1216 using process 1212.
  • Figures 19 and 20 should be referred to in connection with the following description.
  • process conditions 1906 must be maintained at one or more process condition setpoint(s) or aim value(s) (called a regulatory controller setpoint(s) in the example of Figure 14) 1404 so that the product 1216 produced will have the product properties 1904 matching the desired product property aim value(s) 2006.
  • process condition setpoint(s) or aim value(s) called a regulatory controller setpoint(s) in the example of Figure 14
  • the manufacturer must set (step 2008) initial settings of the process condition setpoint(s) (or aim value(s)) 1404 in order for the process 1212 to produce a product 1216 having the desired product property aim values 2006.
  • step 2008 initial settings of the process condition setpoint(s) (or aim value(s)) 1404 in order for the process 1212 to produce a product 1216 having the desired product property aim values 2006.
  • this would be analogous to deciding to set the temperature of the oven to a particular setting before beginning the baking of the cake batter.
  • the second step or aspect involves measurement and adjustment of the process 1212. Specifically, process conditions 1906 must be measured to produce a process condition measurement(s) 1224.
  • the process condition measurement(s) 1224 must be used to generate adjustment(s) 1208 (called controller output data in the example of Figure 12) to a controllable process state(s) 2002 so as to hold the process conditions 1906 as close as possible to process condition setpoint 1404.
  • controller output data in the example of Figure 12
  • this is analogous to the way the oven measures the temperature and turns the heating element on or off so as to maintain the temperature of the oven at the desired temperature value.
  • SUBSTITUTE SHEET The third stage or aspect involves holding a product property measurement(s) of the product properties 1904 as close as possible to the product property aim value(s) 2006. This involves producing product property measurement(s) 1304 based on the product properties 1904 of the product 1216. From these measurements, adjustment to process condition setpoint 1402 to the process condition setpoint(s) 1404 must be made so as to maintain process condition(s) 1906. Referring again to the example above, this would be analogous to measuring how well the cake is baked. This could be done, for example, by sticking a toothpick into the cake and adjusting the temperature during the baking step so that the toothpick eventually comes out clean.
  • time constant is simply an estimate of how quickly control actions must be carried out in order to effectively control the process.
  • process control task can be generalized as being made up of five basic steps or stages as follows:
  • the second and fourth steps or aspects of process control involve measurement 1224 of process conditions 1906 and measurement 1304 of product properties 1904. Such measurements are sometimes very difficult, if not impossible, to effectively perform for process control.
  • the important product properties 1904 relate to the end use of the product and not to the process conditions 1906 of the process 1212.
  • One illustration of this involves the manufacture of carpet fiber.
  • An important product property 1904 of carpet fiber is how uniformly the fiber accepts the dye applied by the carpet maker.
  • Another example involves the cake example set forth above.
  • An important product property 1904 of a baked cake is how well the cake resists breaking apart when the frosting is applied.
  • the measurement of such product properties 1904 is difficult and/or time consuming and/or expensive to make.
  • SUBSTIT are used.
  • such measurements can be unreliable. For example, it may take a number of tests before a reliable result can be obtained. Furthermore, such measurements can also be slow. In this example, it may take so long to conduct the dye test that the manufacturing process can significantly change and be producing different product properties
  • process condition measurements 1224 are inexpensive, take little time, and are quite reliable. Temperature typically can be measured easily, inexpensively, quickly, and reliably. For example, the temperature of the water in a tank can often be easily measured. But oftentimes process conditions 1906 make such easy measurements much more difficult to achieve. For example, it may be difficult to determine the level of a foaming liquid in a vessel. Moreover, a corrosive process may destroy measurement sensors, such as those used to measure pressure.
  • SUBSTITUTE SHEET measurements are used to create values used to control the process 1212 based on inputs that are not identical to the particular process conditions 1906 and/or product properties 1904 that are critical to the control of the process 1212. In other words, these computer models are used to develop predictions (estimates) of the particular process conditions 1906 or product properties 1904. These predictions are used to adjust the controllable process state 2002 or the process condition setpoint 1404.
  • a computer-based fundamental model uses known information about the process 1212 to predict desired unknown information, such as product conditions 1906 and product properties 1904.
  • Empirical Statistical Models Another conventional approach to solving measurement problems is the use of a computer-based statistical model (not shown).
  • Such a computer-based statistical model uses known information about t e process 1212 to determine desired information that cannot be effectively measured.
  • a statistical model is based on the correlation of measurable process conditions 1906 or product properties 1904 of the process 1212.
  • model builder would need to have a base of experience, including known information and actual measurements of desired information.
  • known information is such things as the temperature at which the plastic is processed.
  • Actual measurements of desired information are the actual measurements of the color of the plastic.
  • a mathematical relationship (also called an equation) between the known information and the desired unknown information must be created by the developer of the empirical statistical model.
  • the relationship contains one or more constants (which are assigned numerical values)
  • SUBSTITUTE SHEET which affect the value of the predicted information from any given known information.
  • a computer program uses many different measurements of known information, with their corresponding actual measurements of desired information, to adjust these constants so that the best possible prediction results are achieved by the empirical statistical model.
  • Such computer program can use non-linear regression.
  • Computer-based statistical models can sometimes predict product properties 1904 which are not well described by computer fundamental models.
  • computer statistical models which include the following:
  • the present invention is a modular neural network process control system with natural language configuration.
  • the system allows neural network functions to be easily configured, implemented and operated by a user with no programming expertise.
  • Neural networks are provided as standard module types in a modular control system architecture.
  • Each module has a storage area for holding module parameters and for holding a pointer to a neural network procedure.
  • a set or pre ⁇ defined neural network procedures are provided. Each procedure carries out the real time training and prediction functions of a particular type of neural network. Each procedure can be pointed to by multiple neural network modules, allowing the same basic neural network function to be used with different data in different applications.
  • Neural networks can require large amounts of storage for parameters, such as weight matrices.
  • parameters such as weight matrices.
  • these neural network parameters for each module are stored in a neural network parameter storage area.
  • the module storage which contains the procedure pointer for the module, also holds a pointer to the neural network parameter storage area.
  • a neural network module can be pre-defined or it can be specified by the user.
  • a partial specification of the neural network is presented to the user as a template.
  • the template reflects the aspects of the neural network function which are already defined.
  • the user completes the specification of the neural network function by entering data into fields in the template.
  • the completed specification combined with a procedure implementing the pre ⁇ defined function give a fully operational neural network.
  • Different templates are provided to allow the specification of different types of pre- defined neural network functions.
  • neural network specifications can be accepted using a natural language user interface.
  • the user specifies the function of the neural network using a limited set of substantially natural language constructs.
  • This natural language input is parsed to extract symbolic specifications for the neural network.
  • the symbolic specifications correspond to a library of code fragments or routines, which can be combined to form a fully functional neural network.
  • the input is parsed using key words, associated key words and associated data values. Relational key words can be used to indicate relationships between key word groups.
  • the natural language input is searched for key words and data values. These are analyzed to group appropriate key words and data values to create symbolic specifications corresponding to specific elements of a limited set of allowed symbolic specifications. Parsing is simplified if user input is restricted to one symbolic specification per natural language clause.
  • the neural network can be used for process measurement, supervisory control, or regulatory control functions. Using data pointers, easy access to many process data systems is achieved by the neural network. On-line training can be used to make training the neural network easier. Expert system modules can be included in the modular
  • Figure 1 is a high level block diagram of the six broad steps which make up the computer neural net process control system and method of the present invention.
  • Figure 2 is an intermediate block diagram of important steps and modules which make up the store input data and training input data step and module 102 of Figure 1.
  • Figure 3 is an intermediate block diagram of the important steps and modules which make up the configure and train neural network step and module 104 of Figure 1.
  • Figure 4 is an intermediate block diagram of input steps and modules which make up the predict output data using neural network step and module 106 of Figure 1.
  • Figure 5 is an intermediate block diagram of the important steps and modules which make up the "retrain neural network" step and module 108 of Figure 1.
  • FIG 6 is an intermediate block diagram of the important steps and modules which make up the enable/disable control step and module 110 of Figure 1.
  • FIG 7 is an intermediate block diagram of the important steps and modules which make up the control process using output data step and module 112 of Figure 1.
  • Figure 8 is a detailed block diagram of the configure neural network step and module 302 of the relationship of Figure 3.
  • TE SHEET Figure 9 is a detailed block diagram of the new training input data? step and module 306 of Figure 3.
  • Figure 10 is a detailed block diagram of the train neural network step and module 308 of Figure 3.
  • Figure 11 is a detailed block diagram of the error acceptable? step and module 310 of Figure 3.
  • Figure 12 is a representation of the architecture of an embodiment of the present invention.
  • Figure 13 is a representation of the architecture of an embodiment of the present invention having the additional capability of using laboratory values from the historical database 1210.
  • Figure 14 is an embodiment of controller 1202 of Figures 12 and 13 having a supervisory controller 1408 and a regulatory controller 1406.
  • FIG 15 shows various embodiments of controller 1202 of Figure 14 used in the architecture of Figure 12.
  • Figure 16 is a modular version of block 1502 of Figure 15 showing the various different types of modules that can be utilized with a modular neural network 1206.
  • Figure 17 shows an architecture for block 1502 having a plurality of modular neural networks 1702-1702° with pointers 1710-1710° pointing to a limited set of neural network procedures 1704-1704 m .
  • Figure 18 shows an alternate architecture for block 1502 having a plurality of modular neural networks 1702-1702° with pointers 1710-1710 m to a limited set of neural network procedures 1704-1704°, and with parameter pointers 1802-1802" to a limited set of network parameter storage areas 1806-1806°.
  • Figure 19 is a high level block diagram showing the key aspects of a process 1212 having process conditions 1906 used to produce a product 1216 having product properties 1904 from raw materials 1222.
  • SUBSTITUTE SHEET Figure 20 shows the various steps and parameters which may be used to perform the control of process 1212 to produce products 1216 from raw materials 1222.
  • Figure 21 shows a representative example of a fully connected feed forward neural network 1206 having an input layer 2104, a middle
  • Figure 22 is an exploded block diagram showing the various parameters and aspects that can make up the neural network 1206.
  • Figure 23 is an exploded block diagram of the input data specification 2204 and the output data specification 2206 of the neural network 1206 of Figure 22.
  • Figure 24 is an exploded block diagram of the prediction timing control 2212 and the training timing control 2214 of the neural network 1206 of Figure 22.
  • Figure 25 is an exploded block diagram of various examples and aspects of controller 1202 of Figure 12.
  • Figure 26 is a representative computer display or "screen" of a preferred embodiment of the present invention showing part of the configuration specification of the neural network block 1206.
  • Figure 27 is a representative computer display or "screen" of a preferred embodiment of the present invention showing part of the data specification of the neural network block 1206.
  • Figure 28 is a computer screen which shows a pop-up menu for specifying the data system element of the data specification.
  • Figure 29 is a computer screen of the preferred embodiment showing in detail the individual items making up the data specification display of Figure 27.
  • Figure 30 is a detailed block diagram of an embodiment of the enable control step and module 602 of Figure 6.
  • Figure 31 is a very detailed block diagram of embodiments of steps and modules 802, 804 and 806 of Figure 8.
  • Figure 32 is a very detailed block diagram of embodiments of steps and modules 808, 810, 812 and 814 of Figure 8.
  • Figure 33 is a nomenclature diagram showing the present invention at a high level.
  • Figure 34 shows a representative example of the neural network 1206 of Figure 21 with training capability.
  • Figure 33 This figure is a nomenclature diagram which shows the various names for elements and actions used in describing the present invention.
  • Figure 33 is not necessarily intended to represent the method of the present invention, nor does it necessarily depict the architecture of the present invention. However, it does provide a reference point by which consistent terms can be used in describing the present invention.
  • words that do not appear in boxes which break arrows represent information or data which is being transmitted from one element in the present invention to another.
  • the present invention essentially utilizes neural nets to provide predicted values of important and not readily obtainable process conditions 1906 and/or product properties 1904 to be used by a controller 1202 to produce controller output data 1208 used to control the process 1212.
  • a neural network 1206 operates in conjunction with an historical database 1210 which provides input sensor(s) data 1220.
  • input data and training input data are stored in a historical database with associated timestamps as indicated by a step or module 102.
  • the neural network 1206 is configured and trained in a step or module 104.
  • the neural network 1206 is used to predict output data 1218 using input data 1220, as indicated by a step or module 106.
  • the neural network 1206 is then retrained in a step or module 108, and control using the output data is enabled or disabled in a step or module 110.
  • control of the process using the output data is performed in a step or module 112.
  • the present invention collects and stores the appropriate data, configures and trains the neural network, uses the neural network to
  • the neural network 1206 Central to the present invention is the neural network 1206. Various embodiments of the neural network 1206 can be utilized, and are described in detail below.
  • Neural networks are computer simulations of a network of interconnected neurons.
  • a biological example of interconnected neurons is the human brain.
  • Neural networks are computer representations of architectures which model the working of the brain. It should be understood that the analogy to the human brain is important and useful in understanding the present invention.
  • neural networks used in neural network 1206 of the present invention are computer simulations (or possibly analog devices) which provide useful predicted values based on input data provided at specified intervals.
  • a neural network 1206 is a hierarchical collection of elements, each of which computes the results of an equation (transfer function or activation function).
  • the equation may include a threshold.
  • Each element equation uses multiple input values, but produces only one output value.
  • the outputs of elements in a lower level that is, closer to the input data) are provided as inputs to the elements of higher layers. The highest layer produces the output(s).
  • STITUTE SHEET Figure 21 is merely illustrative of an embodiment of neural network 1206. As discussed below, other embodiments for neural network 1206 can be used.
  • the embodiment of Figure 21 has an input layer 2104, a middle (hidden) layer 2108, and an output layer 2110.
  • Input layer 2104 includes a layer of input elements 2102 which take their input values from the external input data 1220. This is the known information used to produce the predicted values (output data) at outputs 1218. Even though input layer 2104 is referred to as a layer in the neural network 1206, input layer 2104 does not contain any processing elements; instead, it is a set of storage locations for input values on lines 2120.
  • the next layer is called the middle or hidden layer 2108.
  • a middle layer 2108 is not required, but is usually used. It includes a set of elements 2106. The outputs from inputs 2102 of input layer 2104 are used as inputs by each element 2106. Thus, it can be appreciated that the outputs of the previous layer are used to feed the inputs of the next layer.
  • Output layer 2110 has a set of elements 2106. As their input values, they take the output of elements 2106 of the middle layer 2108.
  • the outputs 1218 of elements 2106 of output layer 2110 are the predicted values (called output data) produced by the neural net 1206 using the input data 1220.
  • an adjustable constant called a weight 2112 is defined. For purposes of illustration only, only two weights 2112 are shown. However, each connection between the layers 2104, 2108 and 2110 has an associated weight. Weights determine how much relative effect an input value has on the output value of the element in question.
  • Neural network 1206 is built by specifying the number, arrangement and connection of the elements of which it is made up.
  • the configuration is fairly simple. For example, in a fully connected network with one middle layer (and of course including one input and one output layer), and no feedback, the number of connections and consequently the number of weights is fixed by the number of elements in each layer. Such is the case in the example shown in Figure 21. Since the same equation is usually used in all elements, for this type of network we need to know the number of elements in each layer. This determines the number of weights and hence total storage needed to build the network.
  • the modular aspect of the present invention of Figure 16 takes advantage of this way of simplifying the specification of a neural network. Note that more complex networks require more configuration information, and therefore more storage.
  • the present invention contemplates other types of neural network configurations for use with neural network 1206. All that is required for neural network 1206 is that the neural network be able to be trained and retrained so as to provide the needed predicted values utilized in the process control.
  • Input data 1220 is provided to input storage locations called inputs
  • Middle layer elements 2106 each retrieve the input values from all of the inputs 2102 in the input layer 2104. Each element has a weight 2112 associated with each input value. Each element 2106 multiplies each input value 2102 times its associated weight 2112, and sums these values for all of the inputs. This sum is then used as input to an equation (also called a transfer function or activation function) to produce an output or activation for that element.
  • the processing for elements 2106 in the middle or hidden layer 2108 can be performed in parallel, or they can be performed sequentially. In the neural network with only one middle layer as shown in
  • the output values or activations would then be computed. For each output element 2106, the output values or activations from each of the middle elements 2106 is retrieved. Each output or activation is multiplied by its associated weight 2112, and these values are summed. This sum is then used as input to an equation which produces as its result the output data 1218. Thus using input data 1220, a neural network 1206 produces predicted values of output data 1218.
  • the weights 2112 used in neural network 1206 are adjustable constants which determine (for any given neural network configuration) the values of the predicted output data for given input data. Neural networks are superior to conventional statistical models because neural networks can adjust these weights automatically. Thus, neural networks are capable of building the structure of the relationship (or model) between the input data 1220 and the output data 1218 by adjusting the weights 2112. While a conventional statistical model requires the developer to define the equation(s) in which adjustable constant(s) will be used, the neural network 1206 builds the equivalent of the equation(s) automatically.
  • the present invention contemplates various approaches for training neural network 1206.
  • One suitable approach is back propagation.
  • Back propagation uses the error between the predicted output data 1218 and the associated training input data 1306 as provided by the training set (not shown) to determine how much to adjust the weights 2112 in the network 1206.
  • the error between the predicted output data values and the associated training input data values is propagated back through the output layer 2110 and through the middle layer 2108. This accounts for the name back propagation.
  • the correct output data values are called training input data values.
  • the neural network 1204 is trained by presenting it with a training set(s), which is the actual history of known input data values and the associated correct output data values. As described below, the present invention uses the historical database with its associated timestamps to automatically create a training set(s).
  • the newly configured neural network is usually initialized by assigning random values to all of its weights 2112.
  • the neural network 1206 uses its input data 1220 to produce predicted output data 1218 as described above under Section LB., Prediction.
  • BSTITUTE SHEET functions present in those elements to adjust the weights 2112 between the output layer 2110 and the middle or hidden layer 2108.
  • an error value for each element 2106 in the middle or hidden layer 2108 is computed by summing the errors of the output elements 2106 each multiplied by its associated weight 2112 on the connection between the middle element 2106 in the middle layer 2108 and the corresponding output elements in the output layer 2110.
  • This estimate of the error for each middle (hidden) layer element is then used in the same manner to adjust the weights 2112 between the input layer 2104 and the middle (hidden) layer 2108.
  • Neural networks are superior to computer statistical models because neural networks do not require the developer of the neural network model to create the equations which relate the known input data and training values to the desired predicted values (output data). In other words, neural network 1206 learns the relationships automatically in the training step 104.
  • neural network 1206 requires the collection of training input data with its associated input data, also called training set.
  • the training set must be collected and properly formatted.
  • the conventional approach for doing this is to create a disk file on a computer on which the neural network runs.
  • the present invention is a computer neural network system and method which produces predicted output data values 1218 using a trained network supplied with input data 1220 at a specified interval.
  • the predicted data 1218 is supplied via an historical database 1210 to a controller 1202, which controls a process 1212 which produces a product 1216.
  • the process conditions 1906 and product properties 1904 are maintained at a desired quality level, even though important ones of them can not be effectively measured directly, or modeled using conventional, fundamental or conventional statistical approaches.
  • the present invention can be configured by a developer using a neural network configuration and module 104 step.
  • Various parameters of the neural network can be specified by the developer by using natural language without knowledge of specialized computer syntax and training.
  • the present invention allows an expert in the process being measured to configure the present invention without the use of a neural network expert.
  • the neural network is automatically trained on-line using input data 1220 and associated training input data
  • SUBSTITUTE SHEET 1306 having timestamps (for example, from clock 1230).
  • the input data and associated training input data are stored in an historical database 1210, which supplies this data 1220, 1306 to the neural network 1206 for training at specified intervals.
  • the (predicted) output data value 1218 produced by the neural network is stored in the historical database.
  • the stored output data value 1218 is supplied to the controller 1202 for controlling the process as long as the error data 1504 between the output data 1218 and the training input data 1306 is below an acceptable metric.
  • the error data 1504 is also used for automatically retraining the neural network. This retraining typically occurs while the neural network is providing the controller via the historical database with the output data.
  • the retraining of the neural network results in the output data approaching the training input data as much as possible over the operation of the process. In this way, the present invention can effectively adapt to changes in the process, which can occur in a commercial application.
  • a modular approach for the neural network is utilized to simplify configuration and to produce greater robustness.
  • the modularity is broken out into specifying data and calling subroutines using pointers.
  • data pointers 2204, 2206 are specified.
  • a template approach as shown in Figures 26 and 27, is used to assist the developer in configuring the neural network without having to perform any actual programming.
  • the present invention is an on-line process control system and method.
  • the term "on-line" indicates that the data used in the present invention is collected directly from the data acquisition systems which generate this data.
  • An on-line system may have several characteristics. One characteristic is the processing of data as the data is generated. This may also be called real-time operation. Real-time operation in general
  • SUBSTITUTE SHEET demands that data be detected, processed and acted upon fast enough to effectively respond to the situation.
  • real time means that the data can be responded to fast enough to keep the process in the desired control state.
  • off-line methods can also be used. In off-line methods, the data being used was generated at some point in the past and there is no attempt to respond in a way that can effect the situation. It should be understood that while the preferred embodiment of the present invention uses an on-line approach, alternate embodiments can substitute off-line approaches in various steps or modules.
  • neural networks and neural networks as used in the present invention, combined with the description of the problem of making measurements in a process control environment given in the background section, illustrate that neural networks add a unique and powerful capability to process control systems. They allow the inexpensive creation of predictions of measurements that are difficult or impossible to obtain. This capability opens up a new realm of possibilities for improving quality control in manufacturing processes.
  • neural networks serve as a source of input data to be used by controllers of various types in controlling the process.
  • Expert systems provide a completely separate and completely complimentary capability for process control systems.
  • Expert systems are essentially decision-making programs which base their decisions on process knowledge which is typically represented in the form of if-then rules.
  • Each rule in an expert system makes a small statement of truth, relating something that is known or could be known about the process to something that can be inferred from that knowledge.
  • an expert system can reach conclusions or make decisions which mimic the decision-making of human experts.
  • the preferred method of operation of the present invention stores input data and training data, configures and trains a neural network, predicts output data using the neural network, retrains the neural network, enables or disables control using the output data, and controls the process using output data.
  • more than one step or module is carried out in parallel in the method of the present invention.
  • the divergent order pointer 120 the first two steps or modules in the present invention are carried out in parallel.
  • input data and training input data are stored in the historical database with associated timestamps.
  • the neural network is configured and trained in a step 104.
  • two series of steps or modules are carried out in parallel as indicated by the order pointer 122.
  • the neural network is used to predict output data using input data stored in the historical database.
  • the neural network is retrained using training input data stored in the historical database.
  • control using the output data is enabled or disabled in parallel in a step or module 112, control of the process using the output data is carried out when enabled by step or module 110.
  • an order pointer 120 indicates that a step 102 and a step 104 are performed in parallel.
  • step 102 it is denominated as the store input data and training input data step and module.
  • Figure 2 shows step and module 102 in more detail.
  • step and module 102 has the function of storing input data 1220 and storing training input data 1306.
  • UBSTITUTE SHEET and training input data entry in historical database 1210 utilizes an associated timestamp.
  • the associated timestamp allows the system and method of the present invention to determine the relative time that the particular measurement or predicted value or measured value was taken, produced or derived.
  • FIG. 2 A representative example of step and module 102 is shown in Figure 2, which is described as follows.
  • the order pointer 120 indicates that input data 1220 and training input data 1306 are stored in parallel in the historical database 1210.
  • input data from sensors 1226 are produced by sampling at specific time intervals the sensor signal 1224 provided at the output of the sensor 1226. This sampling produces an input data value or number or signal. Each of these is called an input data 1220 as used in this application.
  • the input data is stored with an associated timestamp in the historical database 1210, as indicated by a step and module 202.
  • the associated timestamp that is stored in the historical database with the input data indicates the time at which the input data was produced, derived, calculated, etc.
  • a step or module 204 shows that the next input data value is stored by step 202 after a specified input data storage interval has lapsed or timed out.
  • This input data storage interval realized by step and module 204 can be set at any specific value. Typically, it is selected based on the characteristics of the process being controlled.
  • training input data in addition to the sampling and storing of input data at specified input data storage intervals, training input data
  • training input data is stored with associated timestamps in the historical database 1210.
  • the associated timestamps utilized with the stored training input data indicate the relative time at which the training input data was derived, produced or obtained. It should be understood that this usually is the time when the process condition or product property
  • SUBSTITUTE SHEET actually existed in the process or product.
  • it since it typically takes a relatively long period of time to produce the training input data (because lab analysis and the like usually has to be performed), it is more accurate to use a timestamp which indicates the actual time when the measured state existed in the process rather than to indicate when the actual training input data was entered into the historical database.
  • This produces a much closer correlation between the training input data 1306 and the associated input data 1220. This close correlation is needed, as is discussed in detail below, in order to more effectively train and control the system and method of the present invention.
  • the training input data is stored in the historical database 1210 in accordance with a specified training input data storage interval, as indicated by a step and module 208. While this can be a fixed time period, it typically is not. More typically, it is a time interval which is dictated by when the training input data is actually produced by the laboratory or other mechanism utilized to produce the training input data 1306. As is discussed in detail herein, this often times takes a variable amount of time to accomplish depending upon the process, the mechanisms being used to produce the training data, and other variables associated both with the process and with the measurement/analysis process utilized to produce the training input data.
  • step and module 102 thus results in the historical database 1210 receiving values of input data and training input data with associated timestamps. These values are stored for use by the system and method of the present invention in accordance with the steps and modules discussed in detail below.
  • the order pointer 120 shows that a configure and train neural network step and module 104 is performed in parallel with the store input data and training input data step and module 102.
  • the purpose of step and module 104 is to configure and train the neural network 1206 (see Figure 12).
  • the order pointer 120 indicates that the step and module 104 plus all of its subsequent steps and modules are performed in parallel to the step and module 102.
  • Figure 3 shows a representative example of the step and module
  • this representative embodiment is made up of five steps and modules 302, 304, 306, 308 and 310.
  • an order pointer 120 shows that the first step and module of this representative embodiment is a configure neural network step and module 302.
  • Configure neural network step and module 302 is used to set up the structure and parameters of the neural network 1206 that is utilized by the system and method of the present invention. As discussed below in detail, the actual steps and modules utilized to set up the structure and perimeters of neural network 1206 are shown in Figure 8.
  • an order pointer 312 indicates that a wait training data interval step and module 304 occurs or is utilized.
  • the wait training data interval step and module 304 specifies how frequently the historical database 1210 will be looked at to determine if there is any new training data to be utilized for training of the neural network 1206. It should be noted that the training data interval of step and module 304 is not the same as the specified training input data storage interval of step and module 206 of Figure 2. Any desired value for the training data interval can be utilized for step and module 304.
  • step and module 306 An order pointer 314 indicates that the next step and module is a new training input data? step and module 306.
  • This step and module 306 is utilized after the lapse of the training data interval specified by step and module 304.
  • the purpose of step and module 306 is to examine the historical database 1210 to determine if new training data has been stored in the historical database since the last time the historical database 1210 was examined for new training data. The presence of new training data will permit the system and method of the present invention to train the neural network 1206 if other parameters/conditions are met.
  • Figure 9 discussed below shows a specific embodiment for the step and module
  • An order pointer 318 indicates that if step and module 306 indicates that new training data is not present in the historical database 1210, the step and module 306 returns the operation of step and module 104 to the step and module 304.
  • the step and module 306 as indicated by an order pointer 316 causes the step and module 104 to move to a train neural network step and module 308.
  • Train neural network step and module 308 is the actual training of the neural network 1206 using the new training data retrieved from the historical database 1210.
  • Figure 10 discussed below in detail, shows a representative embodiment of the train neural network step and module 308.
  • step and module 308 the step and module 104 as indicated by an order pointer 320 moves to an error acceptable? step and module 310.
  • Error acceptable? step and module 310 determines whether the error data 1504 produced by the neural network 1206 is within an acceptable metric, indicating error that the neural network 1206 is providing output data 1218 that is close enough to the training input data 1306 to permit the use of the output data 1218 from the neural network 1206. In other words, an acceptable
  • SUBSTITUTE SHEET error indicates that the neural network 1206 has been "trained" as training is specified by the user of the system and method of the present invention.
  • a representative example of the error acceptable? step and module 310 is shown in Figure 11, which is discussed in detail below. If an unacceptable error is determined by error acceptable? step and module 310, an order pointer 322 indicates that the step and module 104 returns to the wait training data interval step and module 304. In other words, this means that the step and module 104 has not completed training the neural network 1206. Because the neural network 1206 has not yet been trained, training must continue before the system and method of the present invention can move to a step and module 106 discussed below.
  • step and module 310 determines that an acceptable error from the neural network 1206 has been obtained, then the step and module 104 has trained neural network
  • step 104 allows the system and method of the present invention to move to the steps and methods 106 and 112 discussed below.
  • step and module 104 The specific embodiments for step and module 104 are now discussed.
  • Step and module 302 allow the uses of the present invention to both configure and re- configure the neural network.
  • the order pointer 120 indicates that the first step and module is a specify training and prediction timing control step and module 802.
  • Step and module 802 allow the person configuring the system and method of the present invention to specify the training interval(s) and the prediction timing interval(s) of the neural network 1206.
  • step and module 802 can be made up of four steps and modules 3102, 3104, 3106, and 3108.
  • Step and module 3102 is a specify training timing method step and module.
  • the specify training timing method step and module 3102 allows the user configuring the present invention to specify the method or procedure that will be followed to determine when the neural network 1206 will be trained. A representative example of this is when all of the training data has been updated. Another example is the lapse of a fixed time interval. Other methods and procedures can be utilized.
  • An order pointer indicates that a specify training timing parameters step and module 3104 is then carried out by the user of the present invention.
  • This step and module 3104 allows for any needed training timing parameters to be specified.
  • the method or procedure of step and module 3102 can result in zero or more training timing parameters, each of which has a value.
  • This value could be a time value, a module number (in the modular embodiment of the present invention of Figure 16), or a data pointer.
  • the user can configure the present invention so that considerable flexibility can be obtained in how training of the neural network 1206 occurs based on the method or procedure of step and module 3102.
  • An order pointer indicates that once the training timing parameter(s) 3104 has been specified, a specify prediction timing method step and module 3106 is configured by the user of the present invention.
  • This step and module 3106 specifies the method or procedure that will be used by the neural network 1206 to determine when to predict output data values 1218 after it has been trained. This is in contrast to the actual training of the neural network 1206.
  • Representative examples of methods or procedures for step and module 3106 are execute at a fixed time interval, execute after the execution of a specific module, or execute after
  • SUBSTITUTE SHEET a specific data value is updated. Other methods and procedures can be used.
  • An order indicator in Figure 31 shows that a specify prediction timing parameters step and module 3108 is then carried out by the user of the present invention.
  • Any needed prediction timing parameters for the method or procedure of step or module 3106 can be specified.
  • the time interval can be specified as a parameter for the execute at a specific time interval method or procedure.
  • Another example is the specification of a module identifier when the execute after the execution of a particular module method or procedure is specified.
  • Another example is a data pointer when the updating of a data value method or procedure is used.
  • Other operation timing parameters can be used.
  • a specify neural network size step and module 804 is carried out. This step and module 804 allows the user to specify the size and structure of the neural network 1206 that is used by the present invention.
  • step and module 804 a representative example of how the neural network size can be specified by step and module 804 is shown.
  • An order pointer indicates that a specific number of inputs step and module 3110 allows the user to indicate the number of inputs that the neural network 1206 will have. Note that the source of the input data for the specified number of inputs has not yet been fixed by the user in the step and module 3110. Only the actual number of inputs have been specified in the step and module 3110.
  • the user can specify the number of middle (hidden) layer elements in the neural network 1206 by using a step or method 3112.
  • middle elements it is meant that one or more internal layers 2108 of the neural network can be specified by the user.
  • the present invention contemplates a neural network having zero or more middle layers 2108. Typically, one
  • SUBSTITUTE SHEET middle layer is used; however, two or more middle layers are contemplated.
  • An order pointer indicates that once the number of middle elements have been specified in step and module 3112, the number of output data from 2106 the outputs of the neural network 1206 can be specified as indicated by a step or module 3114. Note that where the outputs of the neural network 1206 are to be stored is not specified in step or module 3114. Instead, only the number of outputs are specified in this step of the present invention. As discussed herein, the present invention contemplates any form of presently known or future developed configuration for the structure of the neural network 1206. Thus, steps or modules 3110, 3112, and 3114 can be modified so as to allow the user to specify these different configurations for the neural network 1206.
  • Step and module 806 allows both the training and prediction modes to be specified. It also allows for controlling the storage of the data produced in the training and prediction modes. It also allows for data coordination to be used in training mode.
  • step and module 806 A representative example of the specific training and prediction modes step and module 806 is shown in Figure 31. It is made up of step and modules 3116, 3118, and 3120.
  • an order pointer indicates that the user can specify prediction and train modes in a step and module 3116. These are yes/no or on/off settings. Since the system and method of the present invention is in the train mode at this stage in its operation, step and 3116 typically goes to its default setting of train mode only. However, it should be understood that the present invention contemplates allowing the user to independently control the prediction or train modes.
  • the neural network 1206 When prediction mode is enabled or “on,” the neural network 1206 will predict output data values 1218 using retrieved input data values 1220, as described below. When training mode is enabled or “on,” the neural network 1206 will monitor the historical database 1210 for new training data and will train using the training data, as described below.
  • An order pointer indicates that once the prediction and train modes have been specified in the step and module 3116, the user can specify prediction and train storage modes in a step and module 3118. These are on/off, yes/no values. They allow the user to specify whether the output data produced in the prediction and/or modes will be stored for possible later use. In some situations, the user will specify that they will not be stored, and in such a situation they will be discarded after the prediction or train mode has occurred. Examples of situations where storage may not be needed are as follows. First, if the error acceptable metric value in the train mode indicates that the output data is poor and retraining is necessary, there may be no reason to keep the output data. Another example is in the prediction mode, where the output data is not stored but is only used.
  • An order pointer indicates that a specify training data coordination mode step and module 3120 is then specified by the user. Oftentimes, training input data 1306 must be correlated in some manner with input data 1220. This step and module 3120 allows the user to deal with the relatively long time period required to produce training input data 1306 from when the measured state(s) existed in the process. First, the user can specify whether the most recent input data will be used with the training data, or whether prior input data will be used with the training data. If the user specifies that prior input data is to be used, the method of determining the time of the prior input data can be specified in this step and module 3120.
  • TE SHEET Referring again to Figure 8, once the specified training and prediction modes step and module 806 has been completed by the user, steps and modules 808, 810, 812 and 814 are carried out. Specifically, the user follows a specify input data step and module 808, a specify output data step and module 810, a specify training input data step and module
  • these four steps and modules 808-814 allow the user to specify the source and destination of input and output data for both the (run) prediction and training modes, and the storage location of the error data determined in the training mode.
  • Figure 32 shows a representative embodiment used for all of the steps and modules 808-814 as follows.
  • Steps and modules 3202, 3204 and 3206 essentially are directed to specifying the data location for the data being specified by the user.
  • steps and modules 3208-3216 may be optional in that they allow the user to specify certain options or sanity checks that can be performed on the data as discussed below in more detail.
  • Step or module 3202 is called specify data system.
  • a step or module 3202 is called specify data system.
  • Step or module 3202 allows for the user to specify which computer system(s) contains the data or storage location that is being specified.
  • the user can specify the data type using a specify data type step and module 3204.
  • the data type indicates which of the many types of data and/or storage modes are desired. Examples are current (most recent) values of measurements, historical values, time averaged values, setpoint values, limits, etc.
  • the user can specify a data item number or identifier using a step or module 3206.
  • the data item number or identifier indicates which of the many instances of the specify data type
  • SUBSTITUTE SHEET in the specified data system is desired. Examples are the measurement number, the control loop number, the control tag name, etc. These three steps and modules 3202-3206 thus allow the user to specify the source or destination of the data (used/produced by the neural network) being specified.
  • the user can specify the following additional parameters. Specifically, where data is being specified which is time varying, the user can specify the oldest time interval boundary using a step and module 3208, and can specify the newest time interval boundary using a step and module 3210. For example, these boundaries can be utilized where a time weighted average of a specified data value is needed. Alternatively, the user can specify one particular time when the data value being specified is an historical data point value.
  • Sanity checks on the data being specified can be specified by the user using steps and modules 3212, 3214 and 3216 as follows. Specifically, the user can specify a high limit value using a step and module 3212, and can specify a low limit value using a step and module 3214. Since sensors, for example, sometimes fail, this sanity check allows the user to prevent the system and method of the present invention from using false data from a failed sensor. Other examples of faulty data can also be detected by setting these limits.
  • the high and low limit values can be used for scaling the input data. Neural networks are typically trained and operated using input, output and training input data scaled within a fixed range. Using the high and low limit values allows this scaling to be accomplished so that the scaled values use most of the range. Typical ranges are 0 to 1 and -1 to 1.
  • SUBSTITUTE SHEET can be accomplished by the user specifying a maximum change amount in step and module 3216.
  • Sanity checks can be used in the method of the present invention to prevent erroneous training, prediction, and control. Whenever any data value fails to pass the sanity checks, the data may be clamped at the limit(s), or the operation/control may be disabled. These tests significantly increase the robustness of the present invention.
  • the weights are normally set to random values in their allowed ranges (-1 to 1 is commonly used as a weight range). This can be done automatically, or it can be performed on demand by the user (for example, using softkey 2616 in Figure 26).
  • the wait training input data interval step and module 304 is now described in greater detail.
  • the wait training input data interval is much shorter than the time period (interval) when training input data becomes available.
  • This wait training input data interval determines how often the training input data will be checked to determine whether new training input data has been received. Obviously, the more frequently the training input data is checked, the shorter the time interval will be from when new training input data becomes available to when retraining has occurred.
  • the configuration for the neural network 1206 and specifying its wait training input data interval is done by the user. This interval may be inherent in the software system and method which contains the neural network of the present invention. Preferably,
  • SUBSTITUTE SHEET it is specifically defined by the entire software system and method of the present invention. Now the neural network 1206 is being trained.
  • An order pointer 314 indicates that once the wait training input data interval 304 has elapsed, the new training input data? step or module
  • FIG. 9 shows a representative embodiment of the new training input data? step and module 306.
  • a retrieve current training input timestamp from historical database step and module 902 first retrieves from the historical database 1210 the current training input data timestamp(s).
  • a compare current training input data timestamp to stored training input data timestamp step and module 904 compares the current training input data timestamp(s) with a saved training input data timestamp(s). Note that when the system and method of the present invention is first started, an initialization value must be used for the saved training input data timestamp. If the current training input data timestamp is the same as the saved training input data timestamp, this indicates that new training input data does not exist. This situation on no new training input data is indicated by an order pointer 318.
  • This step and module 904 functions to determine whether any new training input data is available for use in training the neural network. It should be understood that, in various embodiments of the present invention, the presence of new training input data may be detected (determined) in alternate ways. One specific example is where only one storage location is available for training input data and the associated timestamp. In this case, detecting (determining) the presence of new training input data can be carried out by saving internally in the neural
  • SUBSTIT network the associated timestamp of the training input data from the last time the training input data was checked, and periodically retrieving the timestamp from the storage location for the training input data and comparing it to the internally saved value of the timestamp.
  • Other distributions and combinations of storage locations for timestamps and/or data values can be used in detecting (determining) the presence of new training input data.
  • step and module 904 indicates that the current training input data timestamp is different from the saved training input data timestamp, this indicates that new training input data has been received (detected).
  • This new training input data timestamp is saved by a save current training input data timestamp step and module 906. After this current timestamp of training input data has been saved, the new training data? step and module 306 has been completed, and the present invention moves to the train neural network step and module 308 of Figure 3 as indicated by the order pointer.
  • the train neural network step and module 308 is the step and module where the neural network 1206 is trained.
  • Figure 10 shows a representative embodiment of the train neural network step and module 308.
  • an order pointer 316 indicates that a retrieve current training input data from historical database step and module 1002 occurs.
  • one or more current training input data values are retrieved from the historical database 1210.
  • the number of current training input data values that is retrieved is equal to the number of outputs 2106 of the neural network 1206 that is being trained.
  • the training input data is normally scaled. This scaling can use the high and low limit values specified in the configure and train neural network step 104.
  • An order pointer shows that a choose training input data time step and module 1004 is next carried out.
  • the data time (as indicated by their associated timestamps) for them is different. The reason for this is that typically the sampling schedule used to produce the training input data is different for the various training input data. Thus, current training input data often has different associated timestamps. In order to resolve these differences, certain assumptions have to be made. In certain situations, the average between the timestamps is used. Alternately, the timestamp of one of the current training input data could be used. Other approaches also can be employed.
  • the input data at the training input data time is retrieved from the historical database 1210 as indicated by a step and module 1006.
  • the input data is normally scaled. This scaling can use the high and low limit values specified in the configure and train neural network step 104. Thereafter, the neural net 1206 predicts output data from the retrieved input data, as indicated by a step and module 406.
  • the predicted output data from the neural network 1206 is then stored in the historical database 1210, as indicated by a step and module 408.
  • the output data is normally produced in a scaled form, since all the input and training input data is scaled. In this case, the output data must be de-scaled. This de-scaling can use the high and low limit values specified in the configure and train neural network step 104.
  • error data is computed using the output data from the neural network 1206 and the training input data, as indicated by a step and module 1012. It should be noted that the term error data 1504 as used in step and module 1012 is a set of error data value for all of the predicted outputs
  • the present invention also contemplates using a global or cumulative error data for evaluating whether the predicted output data values are acceptable.
  • the neural network 1206 is retrained using the error data 1504 and/or the training input data 1306.
  • the present invention contemplates any method of training the neural network 1306.
  • the error data 1504 is stored in the historical database 1210 in a step and module 1016. It should be noted that the error data 1504 shown here is the individual data for each output 2106. These stored error data 1504 provide a historical record of the error performance for each output 2106 of the neural network 1206.
  • the neural network 1206 may require many presentations of training sets to be adequately (acceptable metric) trained. In this case, two alternate approaches can be used to train the neural network 1206.
  • the neural network 1206 can save the training sets (that is, the training input data and the associated input data which is retrieved in step and module 308) in a database of training sets, which can then be repeatedly presented to the neural network 1206 to train the neural network.
  • the user might be able to configure the number of training sets to be saved. As new training data becomes available, new training sets are constructed and saved. When the specified number of training sets has been accumulated (in a "stack"), the next training set created based on new lab data would "bump" the oldest training set out of the stack. This oldest training set would be discarded.
  • a second (or "stack") approach which can be used is to maintain a time history of input data and training input data in the historical database 1210, and to search the historical database 1210, locating training input data and constructing the corresponding training set by retrieving the associated input data.
  • the combination of the neural network 1206 and the historical database 1210 containing both the input data and the training input data with their associated timestamps provides a very powerful platform for building, training and using the neural network 1206.
  • the present invention contemplates various other modes of using the data in the historical database 1210 and the neural network 1206 to prepare training sets for training the neural network 1206.
  • FIG. 11 shows a representative embodiment of the error acceptable? step and module 310.
  • an order pointer 320 indicates that an compute global error using saved global error step and module 1102 occurs.
  • the term global error as used herein means the error over all the outputs 2106 and/or over two or more training sets (cycles) of the neural network 1206. The global error reduces the effects of variation in the error from one training set (cycle) to the next. One cause for the variation is the inherent variation in lab data tests used to generate the training input data.
  • the global error is saved in a step and module 1104.
  • the global error may be saved internally in the neural network 1206, or it may be stored
  • a step and module 1106 can be used to determine if the global error is statistically different from zero. This step and module 1106 determines whether a sequence of global error values falls within the expected range of variation around the expected (desired) value of zero, or whether the global error is statistically significantly different from zero. This step and module 1106 can be important when the training input data used to compute the global error has significant random variability. If the neural network 1206 is making accurate predictions, the random variability in the training input data (for example, caused by lab variation) will cause random variation of the global error around zero. This step and module 1106 reduces the tendency to incorrectly classify as not acceptable the predicted outputs of the neural network 1206.
  • step and module 104 If the global error is not statistically different from zero, then the global error is acceptable, and the present invention moves to an order pointer 122.
  • An acceptable error indicated by order pointer 122 means that the neural network 1206 is trained. This completes step and module 104.
  • step and module 11008 which is called training input data statistically valid?.
  • step and module 1108 is not needed in the training mode of step and module 104.
  • a global error statistically different from zero moves directly to an order pointer 322.
  • step and module 104 configures and trains the neural network 1206 for use in the present invention.
  • the order pointer 122 indicates that there are two parallel paths that the present invention uses after the configure and train neural network step and module 104.
  • One of the paths, which the predict output data using neural network step and module 106 described below is part of, is used for predicting output data using the neural network 1206, for retraining the neural network 1206 using these predicted output data, and for disabling control of the controlled process when the (global) error from the neural network 1206 exceeds a specified error acceptable metric (criterion).
  • the other path is the actual control of the process using the predicted output data from the neural network 1206.
  • this step and module 106 uses the neural network 1206
  • FIG. 4 shows a representative embodiment of the step and module 106.
  • a wait specified prediction interval step or module 402 utilizes the method or procedure specifed by the user in steps or modules 3106 and 3108 for determining when to retrieve input data.
  • the present invention moves to a retrieve input data at current time from historical database step or module 404.
  • the input data is retrieved at the current time. That is, the most recent value available for each input data value is retrieved from the historical database 1210.
  • the neural network 1206 then predicts output data from the retrieved input data, as indicated by a step and module 406.
  • This output data is used for process control, retraining and control purposes as discussed below in subsequent sections. Prediction is done using any presently known or future developed approach. For example, prediction can be done as specified above in Section LB.
  • a retrain neural network step or module 108 is used.
  • FIG. 5 shows a representative embodiment of the retrain neural network step or module 108.
  • an order pointer 124 shows that a new training input data? step or module 306 determines if new training input data has become available.
  • Figure 9 shows a representative embodiment of the new training input data? step or module 306. Step or module 306 was described above in connection with Figure 3; for this reason, it is not described again here.
  • STITUTE SHEET As indicated by an order pointer 126, if new training data is not present, the present invention returns to the predict output data using neural network step or module 106, as shown in Figure 1.
  • module or step 308 A representative example of module or step 308 is shown in Figure 10. Since training of the neural network is the same as retraining, and has been described in connection with Figure 3, module or step 308 is not discussed in detail here.
  • an order pointer 128 causes the present invention to move to an enable/disable control step or module 110 discussed below.
  • the present invention moves to an enable/disable control step or module 110.
  • the purpose of the enable/disable control step or module 110 is to prevent the control of the process using output data (predicted values) produced by the neural network 1206 when the error is not unacceptable ("poor").
  • FIG. 6 A representative example of the enable/disable control step or module 110 is shown in Figure 6.
  • the function of module 110 is to enable control of the controlled process if the error is acceptable, and to disable control if the error is unacceptable.
  • an order pointer 128 moves the present invention to an error acceptable? step or module 310. If the error between the training input data and the predicted output data is unacceptable, control of the controlled process is disabled by a disable control step and module 604.
  • the disable control step and module 604 sets a flag (indicator) which can be examined by the control process using output data step and module 112 indicating that the output data should not be used for control.
  • SUB Figure 30 shows a representative embodiment of the enable control step and module 602.
  • an order pointer 142 causes the present invention first to move to an output data indicates safety or operability problems? step or module 3002. If the output data does not indicate a safety or operability problem, this indicates that the process 1212 can continue to operate safely. This is indicated by the fact that the present invention moves to the enable control using output data step or module 3006.
  • the present invention recommends that the process being controlled be shut down, as indicated by a recommend process shutdown step and module 3004.
  • This recommendation to the operator of the process 1212 can be made using any suitable approach. An example is a screen display or an alarm indicator. This safety feature allows the present invention to prevent the controlled process 1212 from reaching a critical situation.
  • step and module 3002 If the output data does not indicate safety or operability problems in step and module 3002, or after the recommendation to shut down the process has been made in step and module 3004, the present invention moves to the enable control using output data step and module 3006.
  • This step and module 3006 sets a flag (indicator) which can be examined by step and module 112, indicating that the output data should be used to control the process.
  • the enable/disable control step or module 110 provides the function to the present invention of (1) allowing control of the process 1212 using the output data in step or module 112, (2) preventing the use of the output data in controlling the process 1212, but allowing the process 1212 to continue to operate, or (3) shutting down the process 1212 for safety reasons.
  • the order pointer 122 indicates that the control of the process using the output data from the neural network
  • FIG. 7 shows a representative embodiment of the control process using output data step and module 112.
  • the order pointer 122 indicates that the present invention first moves to a wait controller interval step or module 702.
  • the interval at which the controller operates can be any preselected value. This interval can be a time value, an event, or the occurrence of a data value. Other interval control methods or procedures can be used.
  • step or module
  • control has been disabled by the enable/disable control step or module 110 the present invention does not control the process 1212 using the output data. This is indicated by the order pointer marked "NO" from the control enabled ? step or module 704. If control has been enabled, the present invention moves to the retrieve output data from historical database step or module 706. This step or module shows that the output data 1218 (see Figure 12) produced by the neural network 1206 and stored in the historical database 1210 is retrieved (1214) and used by the controller 1202 to compute controller output data 1208 for control of the process 1212.
  • the present invention effectively controls the process using the output data from the neural network 1206.
  • control of the process 1212 can be any control of the process 1212. It should be understood that the control of the process 1212 can be any control of the process 1212.
  • the process 1212 can continue to be controlled 1202 by the controller without the use of the output data.
  • the preferred embodiment of the present invention comprises one or more software systems.
  • software system is a collection of one or more executable software programs, and one or more storage areas, for example, RAM or disk.
  • a software system should be understood to comprise a fully functional software embodiment of a function, which can be added to an existing computer system to provide new function to that computer system.
  • Software systems generally are constructed in a layered fashion. In a layered system, a lowest level software system is usually the computer operating system which enables the hardware to execute software instructions. Additional layers of software systems may provide, for example, historical database capability. This historical database system provides a foundation layer on which additional software systems can be built. For example, a neural network software system can be layered on top of the historical database. Also, a supervisory control software system can be layered on top of the historical database system.
  • SUBSTITUTE SHEET A software system is thus understood to be a software implementation of a function which can be assembled in a layered fashion to produce a computer system providing new functionality. Also, in general, the interface provided by one software system to another software system is well-defined. It should be understood in the context of the present invention that delineations between software systems are representative of the preferred implementation. However, the present invention may be implemented using any combination or separation of software systems.
  • Figure 12 shows a preferred embodiment of the structure of the present invention. Referring now to Figure 12, the process 1212 being controlled receives the raw materials 1222 and produces the product 1216.
  • Sensors 1226 (of any suitable type) provide sensor signals 1221, 1224, which are supplied to the historical database 1210 for storage with associated timestamps. It should be noted that any suitable type of sensor
  • 1226 can be employed which provides sensor signals 1221, 1224.
  • the historical database 1210 stores the sensor signals 1224 that are supplied to it with associated timestamps as provided by a clock 1230. In addition, as described below, the historical database 1210 also stores output data 1218 from the neural network 1206. This output data 1218 also has associated timestamps provided by the neural network 1206.
  • Historical database 1210 Any suitable type of historical database 1210 can be employed.
  • a historical database is generally discussed in Hale and Sellars, "Historical Data Recording for Process Computers," 77 Chem. Eng'g Progress 38 AICLE, New York, (1981) (which is hereby incorporated by reference).
  • the historical database 1210 that is used must be capable of storing the sensor input data 1224 with associated timestamps, and the predicted output data 1218 from the neural network 1206 with associated timestamps.
  • the historical database 1210 will store the sensor data 1224 in a compressed fashion to reduce storage space requirements, and will store sampled (lab) data 1304 in uncompressed form.
  • the historical database 1210 will be present in a chemical plant in the existing process control system.
  • the present invention can utilize this historical database to achieve the improved process control obtained by the present invention.
  • a historical database is a special type of database in which at least some of the data is stored with associated time stamps. Usually the time stamps can be referenced in retrieving (obtaining) data from a historical database.
  • the historical database 1210 can be implemented as a stand alone software system which forms a foundation layer on which other software systems, such as the neural network 1206, can be layered.
  • a foundation layer historical database system can support many functions in a process control environment.
  • the historical database can serve as a foundation for software which provides graphical displays of historical process data for use by a plant operator.
  • An historical database can also provide data to data analysis and display software which can be used by engineers for analyzing the operation of the process 1212.
  • Such a foundation layer historical database system will often contain a large number of sensor data inputs, possibly a large number of laboratory data inputs, and may also contain a fairly long time history for these inputs.
  • the present invention requires the ability to store at least one training data value with the timestamp which indicates an associated input data value, and the ability to store such an associated input data value.
  • a historical database foundation layer system does not exist, it may be desirable to implement the essential historical database functions as part of the neural network software.
  • the present invention can be implemented in a single software system. It should be understood that
  • the historical database 1210 can be implemented using a number of methods.
  • the historical database can be built as a random access memory (RAM) database.
  • the historical database 1210 can also be implemented as a disk-based database, or as a combination of RAM and disk databases. If an analog neural network 1206 is used in the present invention, the historical database 1210 could be implemented using a physical storage device.
  • the present invention contemplates any computer or analog means of performing the functions of the historical database 1210.
  • the neural network 1206 retrieves input data 1220 with associated timestamps.
  • the neural network 1206 uses this retrieved input data 1220 to predict output data 1218.
  • the output data 1218 with associated timestamps is supplied to the historical database 1210 for storage.
  • a representative embodiment of the neural network 1206 is described above in Section I. It should be understood that neural networks, as used in the present invention, can be implemented in any way. For example, the preferred embodiment uses a software implementation of a neural network 1206. It should be understood, however, that any form of implementing a neural network 1206 can be used in the present invention, including physical analog forms. Specifically, as described below, the neural network may be implemented as a software module in a modular neural network control system.
  • neural network 1206 may be implemented in analog or digital form and also, for example, the controller 1202 may also be
  • SUBSTITUTE SHEET implemented in analog or digital form. It should be understood, with respect to the method steps as described above for the functioning of the systems as described in this section, that operations such as computing (which imply the operation of a digital computer) may also be carried out in analog equivalents or by other methods.
  • the output data 1214 with associated timestamps stored in the historical database 1210 is supplied by a path 1214 to the controller 1202.
  • This output data 1214 is used by the controller 1202 to generate controller output data 1208 sent to an actuator(s) 1228 used to control a controllable process state 2002 of the process 1212. Representative examples of controller 1202 are discussed below.
  • the shaded box shown in Figure 12 indicates that the neural network 1206 and the historical database 1210 may, in a variant of the present invention, be implemented as a single software system.
  • This single software system could be delivered to a computer installation in which no historical database previously existed, to provide the functions of the present invention.
  • a neural network configuration function (or program) 1204 could also be included in this software system.
  • controller 1202 may also be provided with input data 1221 from sensors 1220. This input data is provided directly to controller 1202 from these sensor(s).
  • the neural network configuration module 1204 is connected in a bi-directional path configuration with the neural network
  • the neural network configuration module 1204 is used by the user (developer) to configure and control the neural network 1206 in a fashion as discussed above in connection with the step and module 104 ( Figure 1), or in connection with the user interface discussion contained below.
  • Figure 13 an alternate preferred embodiment of the structure and architecture of the present invention is shown. Only
  • a laboratory (“lab”) 1307 is supplied with samples 1302. These samples 1302 could be physical specimens or some type of data from an analytical test or reading. Regardless of the form, the lab takes this material/data and utilizes it to produce actual measurements 1304, which are supplied to the historical database 1210 with associated timestamps.
  • the values 1304 are stored in the historical database 1210 with their associated timestamps.
  • the historical database 1210 also now contains actual test results or actual lab results in addition to sensor input data.
  • Laboratory data can be electronic data, printed data, or data exchanged over any communications link.
  • the second difference in this embodiment is that the neural network 1206 is supplied with the lab data 1304 and associated timestamps stored in the historical database 1210.
  • Another addition to the architecture of Figure 12 is error data 1504
  • Figure 13 allows the present invention to utilize lab data 1304 as training input data 1306 to train the neural network.
  • controller 1202 utilizes a regulatory controller
  • Any type of regulatory controller is contemplated which provides such regulatory control. There are many commercially available embodiments for such a regulatory controller. Typically, the present invention would be implemented using
  • the embodiment shown in Figure 14 also includes a supervisory controller 1408.
  • the supervisory controller 1408 computes supervisory controller output data, computed in accordancd with the predicted output data 1214. In other words, the supervisory controller 1408 utilizes the predicted output data 1214 from the neural network 1206 to produce supervisory controller output data 1402.
  • the supervisory controller output data 1402 is supplied to the regulatory controller 1406 for changing the regulatory controller setpoint 1404 (or other parameter of regulatory controller 1406). In other words, the supervisory controller output data 1402 is used for changing the regulatory controller setpoint 1404 so as to change the regulatory control provided by the regulatory controller 1406.
  • supervisory controller 1408 Any suitable type of supervisory controller 1408 can be employed by the present invention, including commercially available embodiments. The only limitation is that the supervisory controller 1408 be able to use the output data 1408 to compute the supervisory controller output data 1402 used for changing the regulatory controller setpoint (parameter)
  • the present invention contemplates the supervisory controller 1408 being in a software and hardware system which is physically separate from the regulatory controller 1406.
  • the regulatory controller 1406 is implemented as a digital distributed control system (DCS).
  • DCS digital distributed control system
  • These digital distributed control systems provide a very high level of robustness and reliability for regulating the process 1212.
  • the supervisory controller 1408, in contrast, may be implemented on a host-based computer, such as a VAX (VAX is a trademark of DIGITAL EQUIPMENT CORPORATION, Maynard, Massachusetts).
  • FIG. 15 a more detailed embodiment of the present invention is shown.
  • the supervisory controller 1408 is separated from the regulatory controller 1406.
  • the three shaded boxes shown in Figure 15 suggest various ways in which the functions of the supervisory controller 1408, the neural network configuration program 1204, the neural network 1206 and the historical database 1210 can be implemented.
  • the box labeled 1502 shows how the supervisory controller 1408 and the neural network 1206 can be implemented together in a single software system.
  • This software system may take the form of a modular system as described below in
  • the neural network configuration program 1204 may be included as part of the software system. These various software system groupings are indicative of various ways in which the present invention can be implemented. However, it should be understood that any combination of functions into various software systems can be used to implement the present invention.
  • FIG. 16 a representative embodiment 1502 of the neural network 1206 combined with the supervisory controller 1408 is shown.
  • This embodiment is called a modular supervisory controller approach.
  • the modular architecture that is shown illustrates that the present invention contemplates the use of various types of modules which can be implemented by the user (developer) in configuring neural network(s) 1206 in combination with supervisory control functions so as to achieve superior process control operation.
  • modules that can be implemented by the user of the present invention are shown in the embodiment of Figure 16.
  • the modular embodiment of Figure 16 also includes a feedback control module 1602, a feedforward control module 1604, an expert system module 1606, a cusum (cumulative summation) module 1608, a Shewhart module 1610, a user program module 1612, and a batch event module 1614.
  • a feedback control module 1602 a feedforward control module 1604
  • an expert system module 1606 a cusum (cumulative summation) module 1608, a Shewhart module 1610, a user program module 1612, and a batch event module 1614.
  • SUBSTITUTE SHEET selected by the user.
  • the user can implement more than one of each of these in configuring the present invention.
  • additional types of modules can be utilized.
  • the intent of the embodiment shown in Figure 16 is to illustrate three concepts.
  • the present invention can utilize a modular approach which will ease user configuration of application of the present invention.
  • the modular approach allows for much more complicated systems to be configured since the modules act as basic building blocks which can be manipulated and used independently of each other.
  • the modular approach shows that the present invention can be integrated into other process control systems.
  • the present invention can be implemented into the system and method of the United States patents and patent applications, which are incorporated herein by reference as noted above.
  • this modular approach allows the neural network capability of the present invention to be integrated with the expert system capability described in the above-noted patents and patent applications. As described above, this enables the neural network capabilities of the present invention to be easily integrated with other standard control functions such as statistical tests and feedback and feedforward control. However, even greater function can be achieved by combining the neural network capabilities of the present invention, as implemented in this modular embodiment, with the expert system capabilities of the above- noted patent applications, also implemented in the modular embodiment.
  • SUBSTITUTE SHEET function of the neural network Secondly, the modular approach allows for much easier integration of neural network function with other related control functions, such as feedback control, feedforward control, etc.
  • a modular approach provides a partial definition beforehand of the function to be provided by the neural network module.
  • the predefined function for the module determines the procedures that need to be followed to carry out the module function, and it determines any procedures that need to be followed to verify the proper configuration of the module.
  • the particular function will define the data requirements to complete the specification of the neural network module.
  • the specifications for a modular neural network would be comprised of configuration information which defines the size, connectivity and behavior of the neural network in general, and the data interactions of the neural network which define the source and location of data that will be used and created by the network.
  • Two approaches can be used to simplify the user configuration of neural networks.
  • a limited set of procedures can be prepared and implemented in the modular neural network software.
  • These predefined functions will by nature define the specifications needed to make these procedures work as a neural network module.
  • the creation of a neural network module which is fully connected, has one hidden or middle layer, and has no feedback would require the specification of the number of inputs, the number of middle error elements, and number of outputs. It would not require the specification for the connections between the inputs, the ouputs and elements.
  • This predefined procedure approach which is the preferred method of implementing the modular neural network.
  • a second approach which could be used to provide modular neural network function is to allow a limited set of natural language expressions
  • SUBSTITUTE SHEET to be used to define the neural network.
  • the user or developer would be permitted to enter, through typing or other means, natural language definitions for the neural network.
  • the user may enter the text which might read, for example, "I want a fully connected feedforward neural network.”
  • These user inputs can be parsed by searching for specification combinations of terms, or their equivalents, which would allow the specific configuration information to be extracted from the restricted natural language input.
  • the first approach would be to search for a predefined procedure matching the configuration information provided by the restricted natural language input. This would be useful where users tend to specify the same basic neural network functions for many problems.
  • a second approach could provide for a much more flexible creation of neural network function.
  • the specifications obtained by parsing the natural language input could be used to generate a neural network procedure by actually generating runnable or compilable code.
  • the neural network functions would be defined in relatively small increments as opposed to the approach of providing a complete predefined neural network function.
  • This approach may combine, for example, a small function which is able to obtain input data and populate a set of inputs. By combining a number of such small functional pieces and generating code which reflects and incorporates the user specifications, a complete neural network procedure could be generated.
  • This approach could optionally include the ability to query the user for specifications which have been neglected or omitted in the restricted natural language input. Thus, for example, if the user neglected to specify
  • the parsing and code generation in this approach use pre-defined, small sub-functions of the overall neural network function.
  • a given key word (term) corresponds to a certain sub-function of the overall neural network function.
  • Each sub-function has a corresponding set of key words (terms) and associated key words and numeric values.
  • each key word and associated key words and values constitute a symbolic specification of the neural network sub-function.
  • the collection of all the symbolic specifications make up a symbolic specification of the entire neural network function.
  • the parsing step processes the substantially natural language input. It removes the unnecessary natural language words, and groups the remaining key words and numeric values into symbolic specifications of neural network sub-functions.
  • One way to implement parsing is to break the input into sentences and clauses bounded by periods and commas, and restrict the specification to a single sub-function per clause. Each clause is searched for key words, numeric values, and associated key words. The remaining words are discarded. A given key word (term) corresponds to a certain sub-function of the overall neural network function.
  • key words can have relational tag words, like "in,” “with,” etc., which can indicate the relation of one key word to another.
  • relational tag words like "in,” “with,” etc.
  • the word “in” relates “3 nodes” to "hidden layer,” so that a hidden layer sub-function specification is indicated.
  • multiple sub-function specification could be processed in the same clause.
  • SUBSTITUTE SHEET and an equivalent might be "transfer function.”
  • This key word corresponds to a set of pre-defined sub-functions which implement various kinds of transfer functions in the neural network elements.
  • the specific data that might be allowed in combination with this term might be, for example, the term “sigmoidal” or the word “threshold.” These specific data, combined with the key word, indicate which of the sub-functions should be used to provide the activation function capability in the neural network when it is constructed.
  • nudes which might have an equivalent “nodes” or “elements.”
  • the associated data would be an integer number which indicates the number of nodes in a given layer.
  • various levels of flexibility in the substantially natural language specification can be provided. Increasing levels of flexibility require more detailed and extensive specification of key words and associated data with their associated key words.
  • the neural network itself is constructed, using this method, by processing the specifications, as parsed from the substantially natural language input, probably in a pre-defined order, and generating the fully functional procedure code for the neural network from the procedural sub-function code fragments.
  • the other major advantage of a modular approach is the ease of integration with other functions in the application (problem) domain.
  • the implementation of neural networks as modular neural networks in a larger control system can greatly simplify this kind of implementation.
  • the incorporation of modular neural networks into a modular control system is beneficial because it makes it easy to create and use neural network predictions in a control application.
  • the application of modular neural networks in a control system is different from the control functions that are typically found in a control system.
  • the control functions described in some of the United States patents and patent applications incorporated by reference above generally rely on the current information for their actions, and they do not generally define their function in terms of past data.
  • some means is needed to train and operate the neural network using the data which is not generally available by retrieving current data values.
  • the systems and methods of the present invention, as described above provide this essential capability which allow a modular neural network function to be implemented in a modular control system.
  • a modular neural network has several characteristics which significantly ease its integration with other control functions. First, the execution of neural network functions, prediction and/or training are easily coordinated in time with other control functions. The timing and sequencing capabilities of a modular implementation of a neural network provide this capability. Also, when implemented as a modular function, neural networks can make their results readily accessible to other control functions that may need them. This can be done, for example, without needing to store the neural network outputs in an external system such as an historical database.
  • Modular neural networks can run either synchronized or unsynchronized with other functions in the control system. Any number of neural networks can be created within the same control application, or in different control applications, within the control system. This may
  • SUBSTITUTE SHEET significantly facilitate the use of neural networks to make predictions of output data where several small neural networks may be more easily or rapidly trained than a single large neural network.
  • Modular neural networks also provide a consistent specification and user interface so that a user trained to use the modular neural network control system can address many control problems without learning new software.
  • An extension of the modular concept is the specification of data using pointers.
  • the user is offered the easy specification of a number of data retrieval or data storage functions by simply selecting the function desired and specifying the data needed to implement the function.
  • the retrieval of a time-weighted average from the historical database is one such predefined function.
  • the user need only specify the specific measurement desired, and the starting and ending time boundaries and the predefined retrieval function will use the appropriate code or function to retrieve the data. This significantly simplifies the user's access to data which may reside in a number of different process data systems.
  • the user would have to be skilled in the programming techniques needed to write the calls to retrieve the data from the state assistance.
  • Figure 17 shows the neural network 1206 in a modular form.
  • a specific software embodiment of the modular form of the present invention is shown.
  • a limited set of neural network module types 1702 is provided.
  • Each neural network module type 1702 allows the user to create and configure a neural network module implementing a specific type of neural network.
  • Different types of neural networks may have different connectivity, different numbers of layers of elements, different
  • neural network modules are implemented as data storage areas which contain a procedure pointer 1710', 1710", 1710'" to procedures which carry out the functions of the neural network type used for that module.
  • the neural network procedures 1706' and 1706" are contained in a limited set of neural network procedures 1704.
  • the procedures 1706', 1706" correspond one to one with the neural network types contained in the limited set of neural network types 1702.
  • many neural network modules may be created which use the same neural network procedure.
  • the multiple modules each contain a procedure pointer to the same neural network procedure 1706' or 1706".
  • many modular neural networks can be implemented without duplicating the procedure or code needed to execute or carry out the neural network functions.
  • Figure 18 a more specific software embodiment of the modular neural network is shown. This embodiment is of particular value when the neural network modules are implemented in the same modular software system as modules performing other functions such as statistical tests or feedback control.
  • neural network modules may have significantly greater storage requirements than other module types in the control system. In this case, it is advantageous to store neural network parameters in a separate neural network parameter storage area 1806.
  • each instance of a modular neural network 1702' and 1702" contains two pointers.
  • Each neural network module also contains a second pointer, parameter pointers 1802' anad 1802" which point to a storage area 1806', 1806" for network parameters in a neural network parameter storage area 1804. Only neural network modules need contain the parameter pointers 1802', 1802" to the neural network parameter storage area 1804. Other module types such as control modules which do not require such extensive storage need not have the storage allocated via the parameter pointer 1802.
  • Figure 22 shows representative aspects of the architecture of the neural network 1206. The representation in Figure 22 is particularly relevant in connection with the modular neural network approach shown in Figures 16, 17 and 18 discussed above.
  • the neural network 1206 must contain a neural network model. As stated above, the present invention contemplates all presently available and future developed neural network models and architectures. As shown in Figure 22, the neural network model 2202 can have a fully connected
  • the neural network 1206 must have access to input data and training input data and access to locations in which it can store output data and error data.
  • the preferred embodiment of the present invention is the preferred embodiment of the present invention
  • SUBSTI U E SHEET uses an on-line approach.
  • the data is not kept in the neural network 1206.
  • data pointers are kept in the neural network which point to data storage locations in a separate software system.
  • These data pointers also called data specifications, can take a number of forms and can be used to point to data used for a number of purposes.
  • input data pointer 2204 and output data pointer 2206 must be specified.
  • the pointer can point to or use a particular data source system 2224 for the data, a data type 2226, and a data item pointer 2228.
  • Neural network 1206 must also have a data retrieval function 2208 and a data storage function 2210. Examples of these functions are callable routines 2230, disk access 2232, and network access 2234. These are merely examples of the aspects of retrieval and storage functions. Neural network 1206 must also have prediction timing and training timing. These are specified by prediction timing control 2212 and training timing control 2214. One way to implement this is to use a timing method 2236 and its associated timing parameters 2238. Referring now to Figure 24, examples of timing method 2236 include a fixed time interval 2402, new data entry 2404, after another module 2406, on program request 2408, on expert system request 2410, when all training data updates 2412, and batch sequence methods 2414. These are designed to allow the training and function of the neural network 1206 to be controlled by time, data, completion of modules, or other methods or procedures. The examples are merely illustrative in this regard.
  • Figure 24 also shows examples of the timing parameters 2238. Such examples include the time interval 2416, the module specification 2420, and the sequence specification 2422. Another example is the data item specification (pointer) 2418. As is shown in Figure 24, examples of the data items specification include specifying the data source system
  • training data coordination may also be required in many applications. Examples of approaches that can be used for such coordination are shown. One is to use all current values as representative by reference numeral 2240. Another is to use current training input data values and the input data at the earliest training input data time, as indicated by reference numeral 2242. Another approach is to use the current training input data values with the input data from the latest train time, as indicated by reference numeral 2244. Again, these are merely examples, and should not be construed as limiting in terms of the type of coordination of training data that can be utilized by the present invention.
  • the neural network 1206 also needs to be trained, as discussed above. As stated previously, any presently available or future developed training method is contemplated by the present invention. The training method also may be somewhat dictated by the architecture of the neural network model that is used. Examples of aspects of training methods include back propagation 2246, generalized delta 2248, and gradient descent 2250, all of which are well known in the art. In this regard, reference is made to the article series entitled "Neural Networks Primer,” by Maureen Caudill, Al Expert December 1987 (Part I), February 1988 (Part II), June 1988 (Part III), August 1988 (Part IV), and November 1988 (Part V), all of which are incorporated by reference. Referring now to Figure 23, examples of the data source system
  • examples are an historical database 1210, a distributed control system 1202, a programmable controller 2302, and a networked single loop controller 2304. These are merely illustrative.
  • Any data source system can be utilized by the present invention. It should also be understood that such source system could either be a storage device or an actual measuring or calculating device. All that is required is that a source of data be specified to provide the neural network 1206 with the input data 1220 that is needed to produce the output data 1218.
  • the present invention contemplates more than one data source system used by the same neural network 1206.
  • the neural network 1206 needs to know the data type that is being specified. This is particularly important in an historical database 1210 since it can provide more than one type of data.
  • Figure 23 Several examples are shown in Figure 23 as follows: current value 2306, historical value 2308, time weighted average 2310, controller setpoint 2312, and controller adjustment amount 2314. Other types are contemplated.
  • the data item pointer 2228 must be specified.
  • the examples shown are a loop number 2316, a variable number 2318, a measurement number 2320, and a loop tag I.D. 2322. Again, these are merely examples for illustration purposes, since the present invention contemplates any type of data item pointer 2228.
  • neural network 1206 can be constructed so as to obtain desired input data 1220 or to provide output data 1218 in any intended fashion. In the preferred embodiment of the present invention, this is all done through menu selection by the user (developer) using a software based system on a computer platform.
  • controller 1202 The construction of the controller 1202 is shown in Figure 25 in an exploded format. Again, this is merely for purposes of illustration. First, the controller 1202 must be implemented on some hardware platform 2502. Examples of hardware platforms 2502 include pneumatic single loop controller 2414, electronic single loop controller 2516, networked single looped controller 2518, programmable loop controller 2520, distributed control system 2522, and programmable logic controller 2524.
  • hardware platforms 2502 include pneumatic single loop controller 2414, electronic single loop controller 2516, networked single looped controller 2518, programmable loop controller 2520, distributed control system 2522, and programmable logic controller 2524.
  • controller 1202, 1406, 1408 needs to implement or utilize an algorithm 2504. Any type of algorithm 2504 can be used. Examples shown include: proportional (P)
  • the controller 1202 also inherently includes parameters 2506.
  • Timing means 2508 can use a timing method 2236 with associated timing parameters 2238, as previously described. Again, these are merely illustrative.
  • the controller 1202 also needs to utilize one or more input signals
  • output signals 2510 and to provide one or more output signals 2512.
  • These can take the form of pressure signals 2552, voltage signals 2554, amperage (current) signals 2556, or digital values 2558.
  • input and output signals can be in either analog or digital format.
  • the present invention utilizes a template and menu driven user interface 2600, 2700 which allows the user to configure, reconfigure and operate the present invention. This approach makes the present invention very user friendly. It also eliminates the need for the user to perform any
  • SUBSTITUTE SHEET computer programming since the configuration, reconfiguration and operation of the present invention is carried out in a template and menu format not requiring any actual computer programming expertise or knowledge.
  • the system and method of the present invention utilizes templates.
  • templates define certain specified fields that must be addressed by the user in order to configure, reconfigure and operate the present invention.
  • the templates tend to guide the user in using the present invention.
  • Representative examples of templates for the menu driven system of the present invention are shown in Figures 26-29. These are merely for purposes of illustration.
  • the preferred embodiment of the present invention uses a two- template specification 2600, 2700 for a neural network module.
  • This template 2600 specifies general characteristics of how the neural network 1206 will operate.
  • the portion of the screen within a box labeled 2620 shows how timing options are specified for the neural network module 1206. As previously described, more than one timing option may be provided.
  • This template 2600 provides a training timing option under the label "train” and a prediction timing control specification under the "label run.”
  • the timing methods shown in boldface type are chosen from a pop-up menu of various timing methods that are implemented in the preferred embodiment.
  • Timing method The parameters needed for the timing method which is chosen are entered in the shaded blocks under heading "Time Interval and Key Block.” These parameters are specified only for timing methods for which, they are required. Not all timing methods require parameters, and not all timing methods that require parameters require all the parameters. In a box labeled 2606 bearing the heading "Mode and Store
  • SUBSTITUTE SHEET network module can be controlled. By putting a check in the box next to either the train or the run designation under "Mode,” the training and/or prediction functions of the neural network module 1206 are enabled. By putting a check in the box next to the "when training” and “when running” labels, the storage of predicted output data 1218 can be enabled when the neural network 1206 is training and when the neural network 1206 is predicting (running), respectively.
  • the size of the neural network 1206 is specified in a box labeled 2622 bearing the heading "network size.”
  • a neural network module 1206 there are three layers only, and the user may specify how many elements or nodes are to be used in each layer. In the preferred embodiment, the number of inputs, outputs and middle nodes is limited to some predefined value.
  • the coordination of input data with training data is controlled using a checkbox labeled 2608.
  • the user can specify that input data 1220 is to be retrieved such that the timestamps on the input data 1220 correspond with the timestamps on the training input data 1306.
  • the training or learning constant can be entered in a field 2610. This constant determines how aggressively the weights in the neural network 1206 are adjusted when there is an error 1504 between the output data 1218 and the training input data 1306.
  • the user may call up the second template 2700 in the neural network module specification.
  • This template 2700 is shown in Figure 27,
  • This template 2700 allows the user to specify (1) the data inputs 1220, 1306, and (2) the outputs 1218, 1504 that are to be used by the neural network module.
  • a data specification box 2702, 2704, 2706, and 2708 is provided for each of the network inputs 1220, network training inputs 1306, the network outputs 1218, and the summed error output, respectively. These correspond to the input data, the training input data, the output data, and the error data. These four boxes use the same data specification methods.
  • the data pointers and parameters are specified.
  • the data specification comprises a three-part data pointer as described above.
  • various time boundaries and constraint limits can be specified depending on the data type specified.
  • FIG 28 an example of a pop-up menu is shown.
  • the specification for the data system for the network input number 1 is being specified as shown by the highlighted field reading "DMT PACE.”
  • the box in the center of the screen is a pop-up menu 2802 of choices which may be selected to complete the data system specification.
  • the templates in the preferred embodiment of the present invention utilize such pop-up menus 2802 whereever applicable.
  • Figure 29 shows the various elements which make up the data specification block. These include a data title 2902, an indication as to whether the block is scrollable 2906, and an indication of the number of the specification in a scrollable region 2904.
  • the box also contains arrow pointers indicating that additional data specifications exist in the list either above or below the displayed specification. These pointers 2922 and 2932 are displayed as a small arrow when other data is present. Otherwise, they are blank.
  • the items making up the actual data specification are: a data system 2224, a data type 2226, a data item pointer or number 2228, a name and units label for the data specification 2908, a label 2924, a time boundary 2926 for the oldest time interval boundary, a label 2928, a time specification 2930 for the newest time interval boundary, a label 2910, a high limit 2912 for the data value, a label 2914, a low limit value 2916 for the low limit on the data value, a label 2918, and a value 2920 for the maximum allowed change in the data value.
  • SUBSTITUTE SHEET specification could be used to give more or less flexibility depending on the complexity needed to address the various data sources which may be present.
  • the present invention contemplates any variation on this data specification method.

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Abstract

Procédé et système de commande de processus par réseau neuronal modulaire à configuration en langage natural qui permet aux fonctions du réseau neuronal d'être facilement configurées et utilisées sans expérience de la programmation. Les réseaux neuronaux sont constitués comme des modules standards dans une architecture de système de commande modulaire. Chaque module comprend une zone de stockage servant à contenir des paramètres de modules et un pointeur pour une procédure de réseau neuronal. La procédure effectue l'apprentissage en temps réel et les fonctions de prédiction du réseau neuronal, et elle peut être désignée par plusieurs modules de réseau neuronal. Pour les paramètres de réseau neuronal nécessitant de grandes zones de stockage, telles que les matrices de facteurs de pondération, le stockage du modules contient également un pointeur pour une zone de stockage des paramètres du réseau neuronal. Des fonctions partiellement définies du réseau neuronal sont présentées à l'utilisateur sous forme d'un modèle et l'utilisateur achève la spécification des fonctions du réseau neuronal en introduisant des données dans le modèle. Chaque type de fonction partiellement définie correspond à une procédure de réseau neuronal générique qui est utilisée pour exécuter la fonction du module. Dans un autre mode de fonctionnement, les spécifications du réseau neuronal peuvent être acceptées dans un ensemble limité de structures en langage naturel, qui sont analysées de manière à produire des spécifications symboliques. On crée une procédure de réseau neuronal en combinant les parties de code nécessaires pour la mise en ÷uvre des spéficiations symboliques.
PCT/US1991/005258 1990-08-03 1991-07-25 Systeme de commande de processus par reseau neuronal modulaire a configuration en langage naturel WO1992002896A1 (fr)

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