Identification of stable models via nonparametric prediction error methods

TL;DR

This paper discusses a Bayesian approach to linear system identification focusing on stable predictor estimation, and compares techniques to ensure the stability of the system's impulse response.

Contribution

It introduces and evaluates methods to ensure impulse response stability within a Bayesian predictor estimation framework.

Findings

Techniques effectively improve impulse response stability

Simulation results demonstrate method performance

Bayesian methods enhance system identification stability

Abstract

A new Bayesian approach to linear system identification has been proposed in a series of recent papers. The main idea is to frame linear system identification as predictor estimation in an infinite dimensional space, with the aid of regularization/Bayesian techniques. This approach guarantees the identification of stable predictors based on the prediction error minimization. Unluckily, the stability of the predictors does not guarantee the stability of the impulse response of the system. In this paper we propose and compare various techniques to address this issue. Simulations results comparing these techniques will be provided.