Recursive least squares algorithm and gradient algorithm for Hammerstein–Wiener systems using the data filtering

This paper considers the parameter estimation problems of Hammerstein–Wiener systems by using the data filtering technique. In order to improve the estimation accuracy, the data filtering-based recursive generalized extended least squares algorithm is derived. In order to improve the computational e...

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Vydáno v:	Nonlinear dynamics Ročník 84; číslo 2; s. 1045 - 1053
Hlavní autoři:	Wang, Yanjiao, Ding, Feng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 01.04.2016 Springer Nature B.V
Témata:	Algorithms Automotive Engineering Classical Mechanics Computational efficiency Computer simulation Computing time Control Dynamical Systems Economic models Engineering Estimating techniques Filtering Filtration Least squares Least squares method Mechanical Engineering Nonlinear dynamics Original Paper Parameter estimation Recursive System effectiveness Vibration Gradient search Parameter estimation Nonlinear system Least squares Data filtering
ISSN:	0924-090X, 1573-269X
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Abstract	This paper considers the parameter estimation problems of Hammerstein–Wiener systems by using the data filtering technique. In order to improve the estimation accuracy, the data filtering-based recursive generalized extended least squares algorithm is derived. In order to improve the computational efficiency, the data filtering-based generalized extended stochastic gradient algorithm is derived for estimating the system parameters. Finally, the computational efficiency of the proposed algorithms is analyzed and compared. The simulation results indicate that the proposed algorithms can effectively estimate the parameters of Hammerstein–Wiener systems.
AbstractList	This paper considers the parameter estimation problems of Hammerstein–Wiener systems by using the data filtering technique. In order to improve the estimation accuracy, the data filtering-based recursive generalized extended least squares algorithm is derived. In order to improve the computational efficiency, the data filtering-based generalized extended stochastic gradient algorithm is derived for estimating the system parameters. Finally, the computational efficiency of the proposed algorithms is analyzed and compared. The simulation results indicate that the proposed algorithms can effectively estimate the parameters of Hammerstein–Wiener systems.
Author	Wang, Yanjiao Ding, Feng
Author_xml	– sequence: 1 givenname: Yanjiao surname: Wang fullname: Wang, Yanjiao organization: Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University – sequence: 2 givenname: Feng surname: Ding fullname: Ding, Feng email: fding@jiangnan.edu.cn organization: Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University
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Snippet	This paper considers the parameter estimation problems of Hammerstein–Wiener systems by using the data filtering technique. In order to improve the estimation... This paper considers the parameter estimation problems of Hammerstein-Wiener systems by using the data filtering technique. In order to improve the estimation...
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SubjectTerms	Algorithms Automotive Engineering Classical Mechanics Computational efficiency Computer simulation Computing time Control Dynamical Systems Economic models Engineering Estimating techniques Filtering Filtration Least squares Least squares method Mechanical Engineering Nonlinear dynamics Original Paper Parameter estimation Recursive System effectiveness Vibration
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Title	Recursive least squares algorithm and gradient algorithm for Hammerstein–Wiener systems using the data filtering
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