Stability Orthogonal Regression for System Identification

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 analysed in theory and also demonstrated using two numerical examples in comparison with some popular algorithms.