System Identification Using Kolmogorov-Arnold Networks: A Case Study on Buck Converters
This work addresses system identification in industrial systems like buck converters, offering an incremental improvement through a novel method for interpretable modeling.
The paper tackled system identification for buck converters using Kolmogorov-Arnold Networks (KANs), achieving accurate modeling of dynamics and parameter estimation with improved scalability and interpretability compared to traditional methods.
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.