Hierarchical recursive least squares algorithms for Hammerstein nonlinear autoregressive output‐error systems

Summary This article considers the parameter estimation problem of Hammerstein nonlinear autoregressive output‐error systems with autoregressive moving average noises. Applying the key term separation technique, the original system is decomposed into three subsystems: the first subsystem contains th...

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Bibliographic Details
Published in:International journal of adaptive control and signal processing Vol. 35; no. 11; pp. 2276 - 2295
Main Authors: Kang, Zhen, Ji, Yan, Liu, Ximei
Format: Journal Article
Language:English
Published: Bognor Regis Wiley Subscription Services, Inc 01.11.2021
Subjects:
Algorithms
Autoregressive moving average
Errors
hierarchical identification principle
Least squares
nonlinear system
Parameter estimation
recursive identification
Subsystems
System effectiveness
ISSN:0890-6327, 1099-1115
Online Access:Get full text
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Summary:Summary This article considers the parameter estimation problem of Hammerstein nonlinear autoregressive output‐error systems with autoregressive moving average noises. Applying the key term separation technique, the original system is decomposed into three subsystems: the first subsystem contains the unknown parameters related to the output, the second subsystem contains the unknown parameters related to the input, and the third subsystem contains the unknown parameters related to the noise model. A hierarchical recursive least squares algorithm is proposed based on the hierarchical identification principle for interactively identifying each subsystem. The simulation results confirm that the proposed algorithm is effective in estimating the parameters of Hammerstein nonlinear autoregressive output‐error systems.
Bibliography:Funding information
National Natural Science Foundation of China, 61472195
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0890-6327
1099-1115
DOI:10.1002/acs.3320