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arXiv · 2506.16392

State-Space Kolmogorov Arnold Networks for Interpretable Nonlinear System Identification

Gonçalo Granjal Cruz·Balazs Renczes·Mark C Runacres·Jan Decuyper
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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.

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BibTeXRIS

Gonçalo Granjal Cruz, Balazs Renczes, Mark C Runacres, Jan Decuyper. 2025-06-19. State-Space Kolmogorov Arnold Networks for Interpretable Nonlinear System Identification. https://doi.org/10.1109/lcsys.2025.3578019

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