Parsimonious Identification of Continuous-Time Systems: A Block-Coordinate Descent Approach

TL;DR

This paper introduces a block-coordinate descent method for identifying parsimonious continuous-time systems by decomposing transfer functions, improving interpretability and model simplicity in data-driven system identification.

Contribution

It proposes a novel algorithm that sequentially estimates additive transfer function components, addressing the parsimony challenge in continuous-time system identification.

Findings

The method effectively produces simpler, interpretable models.

Numerical simulations demonstrate the approach's efficacy.

The approach outperforms standard methods in model parsimony.

Abstract

The identification of electrical, mechanical, and biological systems using data can benefit greatly from prior knowledge extracted from physical modeling. Parametric continuous-time identification methods can naturally incorporate this knowledge, which leads to interpretable and parsimonious models. However, some applications lead to model structures that lack parsimonious descriptions using unfactored transfer functions, which are commonly used in standard direct approaches for continuous-time system identification. In this paper we characterize this parsimony problem, and develop a block-coordinate descent algorithm that delivers parsimonious models by sequentially estimating an additive decomposition of the transfer function of interest. Numerical simulations show the efficacy of the proposed approach.