Synchronization sampled-data control of uncertain neural networks under an asymmetric Lyapunov–Krasovskii functional method

Wang, S; Shi, K; Wang, J; Yu, Y; Wen, S; Yang, J; Han, S

Synchronization sampled-data control of uncertain neural networks under an asymmetric Lyapunov–Krasovskii functional method

Wang, S Shi, K Wang, J Yu, Y Wen, S

Yang, J Han, S

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Expert Systems with Applications, 2024, 239
Issue Date:: 2024-04-01

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Field	Value	Language
dc.contributor.author	Wang, S
dc.contributor.author	Shi, K
dc.contributor.author	Wang, J
dc.contributor.author	Yu, Y
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Yang, J
dc.contributor.author	Han, S
dc.date.accessioned	2024-01-16T21:01:33Z
dc.date.available	2024-01-16T21:01:33Z
dc.date.issued	2024-04-01
dc.identifier.citation	Expert Systems with Applications, 2024, 239
dc.identifier.issn	0957-4174
dc.identifier.issn	1873-6793
dc.identifier.uri	http://hdl.handle.net/10453/174608
dc.description.abstract	This paper investigates the synchronization control of neural networks (NNs) considering parameter uncertainties by utilizing an improved asymmetric Lyapunov–Krasovskii functional (ALKF) method. Firstly, an uncertain NNs model with discrete and distributed delays is developed. To synchronize the master–slave system, a memory sampled-data controller is designed. Secondly, an improved ALKF is constructed, in which the positive-definite and symmetric restrictions of matrices are relaxed. Furthermore, improved synchronization criteria are established by linear matrix inequalities (LMIs), which are based on the ALKF method and the integral inequality technique. Finally, two simulation examples are conducted to demonstrate the feasibility and superiority of the established stability conditions in reaching a maximum sampling period.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Expert Systems with Applications
dc.relation.isbasedon	10.1016/j.eswa.2023.122475
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Synchronization sampled-data control of uncertain neural networks under an asymmetric Lyapunov–Krasovskii functional method
dc.type	Journal Article
utslib.citation.volume	239
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2024-01-16T21:01:32Z
pubs.publication-status	Accepted
pubs.volume	239

Abstract:

This paper investigates the synchronization control of neural networks (NNs) considering parameter uncertainties by utilizing an improved asymmetric Lyapunov–Krasovskii functional (ALKF) method. Firstly, an uncertain NNs model with discrete and distributed delays is developed. To synchronize the master–slave system, a memory sampled-data controller is designed. Secondly, an improved ALKF is constructed, in which the positive-definite and symmetric restrictions of matrices are relaxed. Furthermore, improved synchronization criteria are established by linear matrix inequalities (LMIs), which are based on the ALKF method and the integral inequality technique. Finally, two simulation examples are conducted to demonstrate the feasibility and superiority of the established stability conditions in reaching a maximum sampling period.

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http://hdl.handle.net/10453/174608