This repository provides the implementation of the Hierarchical Kolmogorov-Arnold Network (HKAN) model, as presented in the paper HKAN: Hierarchical Kolmogorov-Arnold Network without Backpropagation. To get started quickly, check out the tutorial.

HKAN is a novel network architecture that offers a competitive alternative to the recently proposed Kolmogorov-Arnold Network (KAN). Unlike KAN, which relies on backpropagation, HKAN adopts a randomized learning approach, where the parameters of its basis functions are fixed, and linear aggregations are optimized using least-squares regression. HKAN utilizes a hierarchical multi-stacking framework, with each layer refining the predictions from the previous one by solving a series of linear regression problems. This non-iterative training method simplifies computation and eliminates sensitivity to local minima in the loss function.