Stability orthogonal regression for system identification

Abstract Variable selection methods have been widely used for system identification. However, there is still a major challenge in producing parsimonious models with optimal model structures as popular variable selection methods often produce suboptimal model with redundant model terms. In the paper, stability orthogonal regression (SOR) is proposed to build a more compact model with fewer or no redundant model terms. The main idea of SOR is that multiple intermediate models are produced by orthogonal forward regression (OFR) using sub-sampling technique and then the final model is a combination of these intermediate model terms but does not include infrequently selected terms. The effectiveness of the proposed methods is analyzed in theory and also demonstrated using two numerical examples in comparison with some popular algorithms.