Convergence of the recursive identification algorithms for multivariate pseudo-linear regressive systems

Summary The performance analysis of the recursive algorithms for the multivariate systems with an autoregressive moving average noise process is still open. This paper analyzes the convergence of two recursive identification algorithms, the multivariate recursive generalized extended least squares a...

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Veröffentlicht in:International journal of adaptive control and signal processing Jg. 30; H. 6; S. 824 - 842
Hauptverfasser: Wang, Xuehai, Ding, Feng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bognor Regis Blackwell Publishing Ltd 01.06.2016
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Schlagworte:
Algorithms
Computer simulation
Convergence
Error analysis
least squares algorithm
Least squares method
martingale convergence theorem
Noise
parameter estimation
pseudo-linear regressive model
Recursive
Regression analysis
stochastic gradient algorithm
ISSN:0890-6327, 1099-1115
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Zusammenfassung:Summary The performance analysis of the recursive algorithms for the multivariate systems with an autoregressive moving average noise process is still open. This paper analyzes the convergence of two recursive identification algorithms, the multivariate recursive generalized extended least squares algorithm and the multivariate generalized extended stochastic gradient algorithm, for pseudo‐linear multivariate systems and proves that the parameter estimation errors consistently converge to zero under persistent excitation conditions. The simulation results show that the proposed algorithms work well. Copyright © 2015 John Wiley & Sons, Ltd.
Bibliographie:istex:F4B45B6DF399643062471D1F681336F4BA997D19
PAPD of Jiangsu Higher Education Institutions
ark:/67375/WNG-750Z5TDK-S
ArticleID:ACS2642
Graduate Research Innovation Program of Jiangsu Province - No. KYLX15_1166
National Natural Science Foundation of China - No. 61273194
ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0890-6327
1099-1115
DOI:10.1002/acs.2642