Decomposition-based recursive least squares identification methods for multivariate pseudo-linear systems using the multi-innovation

Ma, Ping; Ding, Feng; Zhu, Quanmin

Abstract

© 2018 Informa UK Limited, trading as Taylor & Francis Group. This paper studies the parameter estimation algorithms of multivariate pseudo-linear autoregressive systems. A decomposition-based recursive generalised least squares algorithm is deduced for estimating the system parameters by decomposing the multivariate pseudo-linear autoregressive system into two subsystems. In order to further improve the parameter accuracy, a decomposition based multi-innovation recursive generalised least squares algorithm is developed by means of the multi-innovation theory. The simulation results confirm that these two algorithms are effective.

• Journal Article Type: Article
• Acceptance Date: Jan 21, 2018
• Online Publication Date: Feb 12, 2018
• Publication Date: Feb 12, 2018
• Deposit Date: Feb 27, 2018
• Publicly Available Date: Feb 12, 2019
• Journal: International Journal of Systems Science
• Print ISSN: 0020-7721
• Electronic ISSN: 1464-5319
• Publisher: Taylor & Francis
• Peer Reviewed: Peer Reviewed
• Volume: 49
• Issue: 5
• Pages: 920-928
• DOI: https://doi.org/10.1080/00207721.2018.1433247
• Keywords: parameter estimation, least squares, multi-innovation, decomposition technique, multivariate system
• Public URL: https://uwe-repository.worktribe.com/output/874945
• Publisher URL: https://doi.org/10.1080/00207721.2018.1433247
• Additional Information: Additional Information : This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Systems Science on 12th February 2018, available online: https://doi.org/10.1080/00207721.2018.1433247.
• Contract Date: Feb 27, 2018