Stability Orthogonal Regression for System Identification

Xiaoquan Tang, Long Zhang

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    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.
    Original languageEnglish
    Pages (from-to)30-36
    Number of pages6
    JournalSystems & Control Letters
    Early online date18 May 2018
    Publication statusPublished - Jul 2018


    • Orthogonal forward regression
    • Stability selection
    • Stability
    • orthogonal regression
    • System identication


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