System Identification Using Kolmogorov-Arnold Networks: A Case Study on Buck Converters

TL;DR

This paper explores the use of Kolmogorov-Arnold Networks for system identification, demonstrating their effectiveness in modeling buck converter dynamics with improved interpretability and accuracy through simulation-based experiments.

Contribution

It introduces a novel application of KANs for modeling and analyzing buck converter systems, highlighting their advantages over traditional methods.

Findings

KANs accurately model buck converter dynamics

Models verify system parameters and detect changes

Demonstrates improved interpretability and scalability

Abstract

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.