Identification of Continuous-Time Hammerstein Systems by Simultaneous Perturbation Stochastic Approximation

This paper proposes an identification method for Hammerstein systems using simultaneous perturbation stochastic approximation (SPSA). Here, the structure of nonlinear subsystem is assumed to be unknown, while the structure of linear subsystem, such as the system order, is assumed to be available. Th...

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Main Authors:	Mohd Ashraf, Ahmad, Azuma, Shun-ichi, Sugie, Toshiharu
Format:	Article
Language:	English
Published:	Elsevier 2016
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/11129/ http://umpir.ump.edu.my/id/eprint/11129/ http://umpir.ump.edu.my/id/eprint/11129/ http://umpir.ump.edu.my/id/eprint/11129/1/Identification%20of%20Continuous-Time%20Hammerstein%20Systems%20by%20Simultaneous%20Perturbation%20Stochastic%20Approximation.pdf

id	ump-11129
recordtype	eprints
spelling	ump-111292018-02-02T08:06:31Z http://umpir.ump.edu.my/id/eprint/11129/ Identification of Continuous-Time Hammerstein Systems by Simultaneous Perturbation Stochastic Approximation Mohd Ashraf, Ahmad Azuma, Shun-ichi Sugie, Toshiharu TK Electrical engineering. Electronics Nuclear engineering This paper proposes an identification method for Hammerstein systems using simultaneous perturbation stochastic approximation (SPSA). Here, the structure of nonlinear subsystem is assumed to be unknown, while the structure of linear subsystem, such as the system order, is assumed to be available. The main advantage of the SPSA-based method is that it can be applied to identification of Hammerstein systems with less restrictive assumptions. In order to clarify this point, piecewise affine functions with a large number of parameters are adopted to approximate the unknown nonlinear subsystems. Furthermore, the linear subsystems are supposed to be described in continuous-time. Though this class of systems closely reflects the actual systems, there are few methods to identify such models. Hence, the SPSA-based method is utilized to identify the parameters in both linear and nonlinear subsystems simultaneously. The effectiveness of the proposed method is evaluated through several numerical examples. The results demonstrate that the proposed algorithm is useful to obtain accurate models, even for high-dimensional parameter identification. Elsevier 2016 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/11129/1/Identification%20of%20Continuous-Time%20Hammerstein%20Systems%20by%20Simultaneous%20Perturbation%20Stochastic%20Approximation.pdf Mohd Ashraf, Ahmad and Azuma, Shun-ichi and Sugie, Toshiharu (2016) Identification of Continuous-Time Hammerstein Systems by Simultaneous Perturbation Stochastic Approximation. Expert Systems with Applications, 43. pp. 51-58. ISSN 0957-4174 http://dx.doi.org/10.1016/j.eswa.2015.08.041 DOI: 10.1016/j.eswa.2015.08.041
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Mohd Ashraf, Ahmad Azuma, Shun-ichi Sugie, Toshiharu Identification of Continuous-Time Hammerstein Systems by Simultaneous Perturbation Stochastic Approximation
description	This paper proposes an identification method for Hammerstein systems using simultaneous perturbation stochastic approximation (SPSA). Here, the structure of nonlinear subsystem is assumed to be unknown, while the structure of linear subsystem, such as the system order, is assumed to be available. The main advantage of the SPSA-based method is that it can be applied to identification of Hammerstein systems with less restrictive assumptions. In order to clarify this point, piecewise affine functions with a large number of parameters are adopted to approximate the unknown nonlinear subsystems. Furthermore, the linear subsystems are supposed to be described in continuous-time. Though this class of systems closely reflects the actual systems, there are few methods to identify such models. Hence, the SPSA-based method is utilized to identify the parameters in both linear and nonlinear subsystems simultaneously. The effectiveness of the proposed method is evaluated through several numerical examples. The results demonstrate that the proposed algorithm is useful to obtain accurate models, even for high-dimensional parameter identification.
format	Article
author	Mohd Ashraf, Ahmad Azuma, Shun-ichi Sugie, Toshiharu
author_facet	Mohd Ashraf, Ahmad Azuma, Shun-ichi Sugie, Toshiharu
author_sort	Mohd Ashraf, Ahmad
title	Identification of Continuous-Time Hammerstein Systems by Simultaneous Perturbation Stochastic Approximation
title_short	Identification of Continuous-Time Hammerstein Systems by Simultaneous Perturbation Stochastic Approximation
title_full	Identification of Continuous-Time Hammerstein Systems by Simultaneous Perturbation Stochastic Approximation
title_fullStr	Identification of Continuous-Time Hammerstein Systems by Simultaneous Perturbation Stochastic Approximation
title_full_unstemmed	Identification of Continuous-Time Hammerstein Systems by Simultaneous Perturbation Stochastic Approximation
title_sort	identification of continuous-time hammerstein systems by simultaneous perturbation stochastic approximation
publisher	Elsevier
publishDate	2016
url	http://umpir.ump.edu.my/id/eprint/11129/ http://umpir.ump.edu.my/id/eprint/11129/ http://umpir.ump.edu.my/id/eprint/11129/ http://umpir.ump.edu.my/id/eprint/11129/1/Identification%20of%20Continuous-Time%20Hammerstein%20Systems%20by%20Simultaneous%20Perturbation%20Stochastic%20Approximation.pdf
first_indexed	2023-09-18T22:11:33Z
last_indexed	2023-09-18T22:11:33Z
_version_	1777415061771386880

Identification of Continuous-Time Hammerstein Systems by Simultaneous Perturbation Stochastic Approximation

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