Ping Ma
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 |
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