Auxiliary Model-Based Multi-Innovation Fractional Stochastic Gradient Algorithm for Hammerstein Output-Error Systems

This paper focuses on the nonlinear system identification problem, which is a basic premise of control and fault diagnosis. For Hammerstein output-error nonlinear systems, we propose an auxiliary model-based multi-innovation fractional stochastic gradient method. The scalar innovation is extended to...

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Autores principales:	Chen Xu, Yawen Mao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	hammerstein output-error systems auxiliary model multi-innovation identification theory fractional-order calculus theory Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/5479d8909bd3422290d2d352ee6d9551
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id	oai:doaj.org-article:5479d8909bd3422290d2d352ee6d9551
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spelling	oai:doaj.org-article:5479d8909bd3422290d2d352ee6d95512021-11-25T18:11:59ZAuxiliary Model-Based Multi-Innovation Fractional Stochastic Gradient Algorithm for Hammerstein Output-Error Systems10.3390/machines91102472075-1702https://doaj.org/article/5479d8909bd3422290d2d352ee6d95512021-10-01T00:00:00Zhttps://www.mdpi.com/2075-1702/9/11/247https://doaj.org/toc/2075-1702This paper focuses on the nonlinear system identification problem, which is a basic premise of control and fault diagnosis. For Hammerstein output-error nonlinear systems, we propose an auxiliary model-based multi-innovation fractional stochastic gradient method. The scalar innovation is extended to the innovation vector for increasing the data use based on the multi-innovation identification theory. By establishing appropriate auxiliary models, the unknown variables are estimated and the improvement in the performance of parameter estimation is achieved owing to the fractional-order calculus theory. Compared with the conventional multi-innovation stochastic gradient algorithm, the proposed method is validated to obtain better estimation accuracy by the simulation results.Chen XuYawen MaoMDPI AGarticlehammerstein output-error systemsauxiliary modelmulti-innovation identification theoryfractional-order calculus theoryMechanical engineering and machineryTJ1-1570ENMachines, Vol 9, Iss 247, p 247 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hammerstein output-error systems auxiliary model multi-innovation identification theory fractional-order calculus theory Mechanical engineering and machinery TJ1-1570
spellingShingle	hammerstein output-error systems auxiliary model multi-innovation identification theory fractional-order calculus theory Mechanical engineering and machinery TJ1-1570 Chen Xu Yawen Mao Auxiliary Model-Based Multi-Innovation Fractional Stochastic Gradient Algorithm for Hammerstein Output-Error Systems
description	This paper focuses on the nonlinear system identification problem, which is a basic premise of control and fault diagnosis. For Hammerstein output-error nonlinear systems, we propose an auxiliary model-based multi-innovation fractional stochastic gradient method. The scalar innovation is extended to the innovation vector for increasing the data use based on the multi-innovation identification theory. By establishing appropriate auxiliary models, the unknown variables are estimated and the improvement in the performance of parameter estimation is achieved owing to the fractional-order calculus theory. Compared with the conventional multi-innovation stochastic gradient algorithm, the proposed method is validated to obtain better estimation accuracy by the simulation results.
format	article
author	Chen Xu Yawen Mao
author_facet	Chen Xu Yawen Mao
author_sort	Chen Xu
title	Auxiliary Model-Based Multi-Innovation Fractional Stochastic Gradient Algorithm for Hammerstein Output-Error Systems
title_short	Auxiliary Model-Based Multi-Innovation Fractional Stochastic Gradient Algorithm for Hammerstein Output-Error Systems
title_full	Auxiliary Model-Based Multi-Innovation Fractional Stochastic Gradient Algorithm for Hammerstein Output-Error Systems
title_fullStr	Auxiliary Model-Based Multi-Innovation Fractional Stochastic Gradient Algorithm for Hammerstein Output-Error Systems
title_full_unstemmed	Auxiliary Model-Based Multi-Innovation Fractional Stochastic Gradient Algorithm for Hammerstein Output-Error Systems
title_sort	auxiliary model-based multi-innovation fractional stochastic gradient algorithm for hammerstein output-error systems
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5479d8909bd3422290d2d352ee6d9551
work_keys_str_mv	AT chenxu auxiliarymodelbasedmultiinnovationfractionalstochasticgradientalgorithmforhammersteinoutputerrorsystems AT yawenmao auxiliarymodelbasedmultiinnovationfractionalstochasticgradientalgorithmforhammersteinoutputerrorsystems
_version_	1718411497411969024

Auxiliary Model-Based Multi-Innovation Fractional Stochastic Gradient Algorithm for Hammerstein Output-Error Systems

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