Comparison of Regularized Least-Squares Approach and Predictive Error Method: Case Studies * *This work is supported by the National Science Foundation of China (No. 61273191) and by 973 Program of China (No. 2012CB720500). Corresponding author Yucai Zhu. Tel. +15381085960. Fax +0571-87953353.

Abstract

Abstract There are recently studies on linear system identification with high order finite impulse response (FIR) models using the regularized least-squares approach. The regularized approach allows some bias to reduce variance so that the error of the regularized FIR model is minimized. This paper concerns the comparison between the regularized least-square approach and the traditional predictive error method (PEM), using a number of case studies. More cases are included than those used by the authors of the regularized approach, for example, non-white input signal, non-white output noise, low-order systems and closed-loop tests. In most cases PEM outperforms the regularized approach.