SS-KAN通过状态空间整合KAN，提升非线性系统识别的可解释性。
State-Space Kolmogorov Arnold Networks for Interpretable Nonlinear System Identification
- 在状态空间中嵌入KAN，利用一元函数显式建模系统非线性。
- 在Silverbox和Wiener-Hammerstein数据集上实现可解释性提升，精度略低于黑盒模型。
- 适合需要理解系统动态机制的研究者，尤其关注模型透明性的场景。
尽管精确，黑箱系统识别模型缺乏对系统动态的可解释性。本文提出状态空间柯尔莫哥洛夫-阿诺德网络（SS-KAN），通过将柯尔莫哥洛夫-阿诺德网络融入状态空间框架来解决该问题。模型在两个基准系统——Silverbox和Wiener-Hammerstein上进行了验证。结果表明，得益于稀疏正则化和学习到的一元函数直接可视化，SS-KAN显著提升了可解释性，虽然在精度上略逊于当前最先进的黑箱模型，但展现出在非线性系统动态的准确性和可解释性之间取得平衡的潜力。
原文摘要 · Abstract (English)
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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