Improved least-squares identification for multiple-output non-linear stochastic systems

• Published in: IET control theory & applications 2020-04, Vol.14 (7), p.964-971
• Main Authors: Xia, Huafeng, Ji, Yan, Yang, Yongqing, Ding, Feng, Hayat, Tasawar
• Format: Article
• Language: English
• Subjects: computational complexity; hierarchical identification principle; identification problems; improved least‐squares identification; key term separation technique; least squares approximations; linear parts; linear systems; maximum likelihood estimation; maximum likelihood‐based estimation algorithm; model decomposition technique; multiple‐output nonlinear equation‐error moving average systems; multiple‐output nonlinear stochastic systems; nonlinear equations; nonlinear part; nonlinear systems; parameter estimation; recursive estimation; recursive extended least‐squares estimation algorithm; reduced computational complexity; Research Article; stochastic systems
• ISSN: 1751-8644; 1751-8652; 1751-8652
• DOI: 10.1049/iet-cta.2019.0915

This study considers the identification problems of multiple-output non-linear equation-error moving average systems. There exist the product items of the parameters between the non-linear and linear parts. To solve this difficulty, the key term separation technique is adopted. By using the model decomposition technique and the hierarchical identification principle, a maximum likelihood-based recursive extended least-squares estimation algorithm with reduced computational complexity is presented to estimate the parameters of the non-linear part and the linear part interactively. The simulation results demonstrate the effectiveness of the proposed method.