Finite-time dissipative control for discrete-time memristive neural networks via interval matrix method

Yang, J; Chen, G; Wen, S; Wang, L

Finite-time dissipative control for discrete-time memristive neural networks via interval matrix method

Yang, J Chen, G Wen, S

Wang, L

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Chaos, Solitons and Fractals, 2023, 176
Issue Date:: 2023-11-01

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Field	Value	Language
dc.contributor.author	Yang, J
dc.contributor.author	Chen, G
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Wang, L
dc.date.accessioned	2024-01-16T21:14:26Z
dc.date.available	2024-01-16T21:14:26Z
dc.date.issued	2023-11-01
dc.identifier.citation	Chaos, Solitons and Fractals, 2023, 176
dc.identifier.issn	0960-0779
dc.identifier.issn	1873-2887
dc.identifier.uri	http://hdl.handle.net/10453/174623
dc.description.abstract	This paper addresses the problems of finite-time dissipative analysis and control for discrete-time memristive neural networks (DMNNs). With the help of interval matrix method (IMM), the challenges posed by the mismatched state-dependent parameters of DMNNs can be solved, which is different from the maximal absolute value operation-based method (MAVOM) in most existing literature. Based on a discrete-time Lyapunov-Krasovskii functional (LKF) and some inequality techniques, several sufficient conditions are established for achieving both finite-time bounded (FTB) behavior and finite-time (Q,S,R)−γ dissipative (FTD). Moreover, the control gains are obtained by solving a series of linear matrix inequalities (LMIs) and convex optimization problems. Finally, the validity of our main findings and the superiority of the control strategies are verified through numerical simulations.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Chaos, Solitons and Fractals
dc.relation.isbasedon	10.1016/j.chaos.2023.114161
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Mathematical Physics
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.subject.classification	49 Mathematical sciences
dc.title	Finite-time dissipative control for discrete-time memristive neural networks via interval matrix method
dc.type	Journal Article
utslib.citation.volume	176
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	2024-01-16T21:14:25Z
pubs.publication-status	Published
pubs.volume	176

Abstract:

This paper addresses the problems of finite-time dissipative analysis and control for discrete-time memristive neural networks (DMNNs). With the help of interval matrix method (IMM), the challenges posed by the mismatched state-dependent parameters of DMNNs can be solved, which is different from the maximal absolute value operation-based method (MAVOM) in most existing literature. Based on a discrete-time Lyapunov-Krasovskii functional (LKF) and some inequality techniques, several sufficient conditions are established for achieving both finite-time bounded (FTB) behavior and finite-time (Q,S,R)−γ dissipative (FTD). Moreover, the control gains are obtained by solving a series of linear matrix inequalities (LMIs) and convex optimization problems. Finally, the validity of our main findings and the superiority of the control strategies are verified through numerical simulations.

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http://hdl.handle.net/10453/174623