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

Ma, Ping; Ding, Feng; Zhu, Quanmin

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


Authors

Ping Ma

Feng Ding

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Quan Zhu Quan.Zhu@uwe.ac.uk
Professor in Control Systems



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.

Citation

Ma, P., Ding, F., & Zhu, Q. (2018). Decomposition-based recursive least squares identification methods for multivariate pseudo-linear systems using the multi-innovation. International Journal of Systems Science, 49(5), 920-928. https://doi.org/10.1080/00207721.2018.1433247

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 Mar 28, 2024
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.

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