State-Space Kolmogorov Arnold Networks for Interpretable Nonlinear System Identification
Abstract
While accurate, black-box system identification models lack interpretability of the underlying system dynamics. This paper proposes State-Space Kolmogorov-Arnold Networks (SS-KAN) to address this challenge by integrating Kolmogorov-Arnold Networks within a state-space framework. The proposed model is validated on two benchmark systems: the Silverbox and the Wiener-Hammerstein benchmarks. Results show that SS-KAN provides enhanced interpretability due to sparsity-promoting regularization and the direct visualization of its learned univariate functions, which reveal system nonlinearities at the cost of accuracy when compared to state-of-the-art black-box models, highlighting SS-KAN as a promising approach for interpretable nonlinear system identification, balancing accuracy and interpretability of nonlinear system dynamics.
Keywords
Cite
@article{arxiv.2506.16392,
title = {State-Space Kolmogorov Arnold Networks for Interpretable Nonlinear System Identification},
author = {Gonçalo Granjal Cruz and Balazs Renczes and Mark C Runacres and Jan Decuyper},
journal= {arXiv preprint arXiv:2506.16392},
year = {2025}
}
Comments
Accepted for IEEE Control Systems Letters