Data Driven Estimation of Stochastic Switched Linear Systems of Unknown Order

TL;DR

This paper presents a data-driven method for estimating the parameters of stochastic switched linear systems with unknown order, using Hankel-like matrices and SVD to achieve accurate system identification from noisy data.

Contribution

It introduces a novel approach combining subspace methods and model order selection for stable SLS with unknown dimension, avoiding non-convex optimization issues.

Findings

Accurate estimation of system parameters improves with more data.

The method effectively determines the unknown system order.

Estimates are close to balanced truncated realizations with high probability.

Abstract

We address the problem of learning the parameters of a mean square stable switched linear systems (SLS) with unknown latent space dimension, or \textit{order}, from its noisy input--output data. In particular, we focus on learning a good lower order approximation of the underlying model allowed by finite data. Motivated by subspace-based algorithms in system theory, we construct a Hankel-like matrix from finite noisy data using ordinary least squares. Such a formulation circumvents the non-convexities that arise in system identification, and allows for accurate estimation of the underlying SLS as data size increases. Since the model order is unknown, the key idea of our approach is model order selection based on purely data dependent quantities. We construct Hankel-like matrices from data of dimension obtained from the order selection procedure. By exploiting tools from theory of model reduction for SLS, we obtain suitable approximations via singular value decomposition (SVD) and show that the system parameter estimates are close to a balanced truncated realization of the underlying system with high probability.