Recursive Identification of Hammerstein Systems with Polynomial Function Approximation
International Journal of Management and Fuzzy Systems
Volume 3, Issue 6, December 2017, Pages: 87-94
Received: Sep. 25, 2017; Accepted: Oct. 27, 2017; Published: Nov. 20, 2017
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Authors
Wang Jian-hong, School of Electronic Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, China
Tang De-zhi, School of Electrical and Information Engineering, Anhui University of Technology, Ma-an-shan, China
Jiang Hong, School of Electronic Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, China
Tang Xiao-jun, School of Electronic Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, China
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Abstract
Nonlinear system identification is considered, where the nonlinear static function was approximated by a number of polynomial functions. It is based on a piecewise-linear Hammerstein model, which is linear in the parameters. The identification procedure is divided into two steps. Firstly we adopt the extended stochastic gradient algorithm to identify some unknown parameters. Secondly using singular value decomposition (SVD), we propose a new method to identify other parameters. The basic idea is to replace un-measurable noise terms in the information vectors by their estimates, and to compute the noise estimates based on the obtained parameter estimates. The applicability of the approach is illustrated by a simulation.
Keywords
Nonlinear System, Hammerstein Systems, Polynomial Functions Approximation, Recursive Identification, Singular Value Decomposition
To cite this article
Wang Jian-hong, Tang De-zhi, Jiang Hong, Tang Xiao-jun, Recursive Identification of Hammerstein Systems with Polynomial Function Approximation, International Journal of Management and Fuzzy Systems. Vol. 3, No. 6, 2017, pp. 87-94. doi: 10.11648/j.ijmfs.20170306.12
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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