用可解释神经网络精准建模直流降压电路动态特性
System Identification Using Kolmogorov-Arnold Networks: A Case Study on Buck Converters
- 基于柯尔莫戈洛夫-阿诺德定理，用可学习激活函数逼近系统状态变化
- 准确识别电路参数，验证模型一致性并检测参数漂移
- 适合需要可解释性的工业控制系统建模与故障检测
Kolmogorov-Arnold Networks（KANs）作为一种新兴的可解释且高效的动态系统建模框架，利用柯尔莫戈洛夫-阿诺德表示定理，通过可学习的激活函数实现函数逼近，相比传统神经网络具有更好的可扩展性、精度和可解释性。本文研究了KAN在直流降压转换器系统中的应用，重点在于状态空间参数估计及系统方程的发现。基于仿真数据，方法包括使用KAN近似状态导数，构建可解释的状态空间模型，并通过数值实验进行验证。结果表明，KAN能够准确识别系统动态特性，验证模型一致性，并检测参数变化，为现代工业系统中的系统辨识提供了重要参考。
原文摘要 · Abstract (English)
Kolmogorov-Arnold Networks (KANs) are emerging as a powerful framework for interpretable and efficient system identification in dynamic systems. By leveraging the Kolmogorov-Arnold representation theorem, KANs enable function approximation through learnable activation functions, offering improved scalability, accuracy, and interpretability compared to traditional neural networks. This paper investigates the application of KANs to model and analyze the dynamics of a buck converter system, focusing on state-space parameter estimation along with discovering the system equations. Using simulation data, the methodology involves approximating state derivatives with KANs, constructing interpretable state-space representations, and validating these models through numerical experiments. The results demonstrate the ability of KANs to accurately identify system dynamics, verify model consistency, and detect parameter changes, providing valuable insights into their applicability for system identification in modern industrial systems.
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