Auxiliary model‐based recursive least squares algorithm for two‐input single‐output Hammerstein output‐error moving average systems by using the hierarchical identification principle

This article considers the parameter estimation problems of two‐input single‐output Hammerstein output‐error moving average systems. The system is decomposed into two subsystems based on the hierarchical principle. The first model is used to identify the linear parameters and the parameters of the u...

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Vydáno v:	International journal of robust and nonlinear control Ročník 32; číslo 13; s. 7575 - 7593
Hlavní autoři:	Liu, Jian, Ji, Yan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Bognor Regis Wiley Subscription Services, Inc 10.09.2022
Témata:	Algorithms auxiliary model Least squares Mathematical models Model accuracy multi‐innovation identification theory nonlinear system Parameter estimation Parameter identification Principles recursive identification Subsystems
ISSN:	1049-8923, 1099-1239
On-line přístup:	Získat plný text
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Abstract	This article considers the parameter estimation problems of two‐input single‐output Hammerstein output‐error moving average systems. The system is decomposed into two subsystems based on the hierarchical principle. The first model is used to identify the linear parameters and the parameters of the unknown measurable information vector. The second model is for identifying non‐linear parameters. By using the auxiliary model, we introduce a forgetting factor to improve the parameter estimation accuracy. The auxiliary model‐based forgetting factor recursive least squares algorithm and the auxiliary model‐based forgetting factor multi‐innovation recursive least squares algorithm are presented. The simulation results indicate that the proposed algorithms are effective.
AbstractList	This article considers the parameter estimation problems of two‐input single‐output Hammerstein output‐error moving average systems. The system is decomposed into two subsystems based on the hierarchical principle. The first model is used to identify the linear parameters and the parameters of the unknown measurable information vector. The second model is for identifying non‐linear parameters. By using the auxiliary model, we introduce a forgetting factor to improve the parameter estimation accuracy. The auxiliary model‐based forgetting factor recursive least squares algorithm and the auxiliary model‐based forgetting factor multi‐innovation recursive least squares algorithm are presented. The simulation results indicate that the proposed algorithms are effective.
Author	Liu, Jian Ji, Yan
Author_xml	– sequence: 1 givenname: Jian surname: Liu fullname: Liu, Jian organization: Qingdao University of Science and Technology – sequence: 2 givenname: Yan orcidid: 0000-0002-3245-2437 surname: Ji fullname: Ji, Yan email: yjichina@163.com organization: Qingdao University of Science and Technology
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Snippet	This article considers the parameter estimation problems of two‐input single‐output Hammerstein output‐error moving average systems. The system is decomposed...
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SubjectTerms	Algorithms auxiliary model Least squares Mathematical models Model accuracy multi‐innovation identification theory nonlinear system Parameter estimation Parameter identification Principles recursive identification Subsystems
Title	Auxiliary model‐based recursive least squares algorithm for two‐input single‐output Hammerstein output‐error moving average systems by using the hierarchical identification principle
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