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Passivity-Based State Estimation of Markov Jump Singularly Perturbed Neural Networks Subject to Sensor Nonlinearity and Partially Known Transition Rates

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Abstract

In this paper, the passivity-based state estimation problem is investigated for Markov jump singularly perturbed neural networks, in which the partially known transition rate matrix and the nonlinear characteristics of sensors are considered simultaneously. By using a new inequality, a novel perturbed parameter dependent Lyapunov function is constructed for Markov jump singularly perturbed neural networks. Based on those, some sufficient conditions are established to guarantee the stochastically mean-square stable for the considered system with the property of passivity. Besides, a less conservativeness state estimator design method is established for Markov jump singularly perturbed neural networks subject to sensor nonlinearity and partially known transition rates. At last, a numerical example is presented to demonstrate the validity of the obtained results.

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Author information

Authors and Affiliations

  1. Anhui Province Key Laboratory of Special Heavy Load Robot, Anhui University of Technology, Ma’anshan, 243032, China

    Ya-Nan Wang, Feng Li & Lei Su

  2. School of Electrical and Information Engineering, Anhui University of Technology, Ma’anshan, 243032, China

    Ya-Nan Wang, Feng Li, Lei Su & Rongsheng Xia

Authors
  1. Ya-Nan Wang
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  2. Feng Li
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  3. Lei Su
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  4. Rongsheng Xia
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Contributions

Y-NW, the role: Writing—original draft and review and editing; Methodology; Investigation; Visualization; FL, the role: Writing—original draft and review and editing; Conceptualization Methodology; Funding acquisition and Project administration; LS, the role: Writing—review and editing; Methodology; RX, the role: Writing—review and editing; Validation.

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Correspondence to Feng Li.

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This work was supported by the National Natural Science Foundation of China under Grants 62303016, 62273006, 62173001, 62103005, 61873002, 61703004 the Natural Science Foundation for Distinguished Young Scholars of Higher Education Institutions of Anhui Province under grant 2022AH020034, the Natural Science Foundation for Excellent Young Scholars of Higher Education Institutions of Anhui Province under grant 2022AH030049, the Major Natural Science Foundation of Higher Education Institutions of Anhui Province under grant KJ2020ZD28, Natural Science Foundation for Excellent Young Scholars of Anhui Province 2108085Y21, the Major Technologies Research and Development Special Program of Anhui Province under Grant 202003a05020001, the Key research and development projects of Anhui Province under Grant 202104a05020015, the Natural Science Foundation for Excellent Young Scholars of Anhui Province 2023AH030030, the Anhui Provincial Natural Science Foundation under grant 2208085QF202, the Scientific Research Projects in Colleges and Universities of Anhui Province under Grant 2022AH050308, the Open Project of China International Science and Technology Cooperation Base on Intelligent Equipment Manufacturing in Special Service Environment under Grant ISTC2021KF04, the Open Fund of Anhui Province Key Laboratory of Special Heavy Load Robot under Grant TZJQR002-2023, the Open Fund Project of Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes under Grant CS2022-03.

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Wang, YN., Li, F., Su, L. et al. Passivity-Based State Estimation of Markov Jump Singularly Perturbed Neural Networks Subject to Sensor Nonlinearity and Partially Known Transition Rates. Neural Process Lett 55, 12205–12222 (2023). https://doi.org/10.1007/s11063-023-11416-9

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  • DOI: https://doi.org/10.1007/s11063-023-11416-9

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