arXiv Open Access 2025

State-Space Kolmogorov Arnold Networks for Interpretable Nonlinear System Identification

Gonçalo Granjal Cruz Balazs Renczes Mark C Runacres Jan Decuyper

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Abstrak

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.

Topik & Kata Kunci

cs.LG eess.SY

Penulis (4)

Gonçalo Granjal Cruz

Balazs Renczes

Mark C Runacres

Jan Decuyper

Format Sitasi

APA MLA BibTeX

Cruz, G.G., Renczes, B., Runacres, M.C., Decuyper, J. (2025). State-Space Kolmogorov Arnold Networks for Interpretable Nonlinear System Identification. https://arxiv.org/abs/2506.16392

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Informasi Jurnal

Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓