Fixed/predefined-time synchronization of memristive neural networks based on state variable index coefficient

Xiao, J; Hu, Y; Zeng, Z; Wu, A; Wen, S

Fixed/predefined-time synchronization of memristive neural networks based on state variable index coefficient

Xiao, J Hu, Y Zeng, Z Wu, A Wen, S

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Neurocomputing, 2023, 560
Issue Date:: 2023-12-01

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Field	Value	Language
dc.contributor.author	Xiao, J
dc.contributor.author	Hu, Y
dc.contributor.author	Zeng, Z
dc.contributor.author	Wu, A
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.date.accessioned	2024-01-16T21:09:44Z
dc.date.available	2024-01-16T21:09:44Z
dc.date.issued	2023-12-01
dc.identifier.citation	Neurocomputing, 2023, 560
dc.identifier.issn	0925-2312
dc.identifier.issn	1872-8286
dc.identifier.uri	http://hdl.handle.net/10453/174618
dc.description.abstract	This paper has investigated the fixed/predefined-time synchronization problem of delayed memristive neural networks(DNNs). Firstly, two novel controllers with state variable index coefficient are presented for achieving fixed-time synchronization of driver-response systems. Then, one effective control scheme is proposed out to realize predefined-time synchronization based on the results of fixed-time synchronization. Meanwhile, some conditions are given to direct how to select suitable controllers’ parameters by employing fixed-time and Lyapunov theory. The established control schemes are shown to have simpler structures and may need lower energy. In addition, the settling time of fixed time synchronization has been estimated independently on initial states while predefined-time can be set by user in advance. At last, numerical experiments are conducted to verify the effectiveness of the results.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Neurocomputing
dc.relation.isbasedon	10.1016/j.neucom.2023.126849
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.subject.classification	52 Psychology
dc.title	Fixed/predefined-time synchronization of memristive neural networks based on state variable index coefficient
dc.type	Journal Article
utslib.citation.volume	560
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	17 Psychology and Cognitive Sciences
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	2024-01-16T21:09:42Z
pubs.publication-status	Published
pubs.volume	560

Abstract:

This paper has investigated the fixed/predefined-time synchronization problem of delayed memristive neural networks(DNNs). Firstly, two novel controllers with state variable index coefficient are presented for achieving fixed-time synchronization of driver-response systems. Then, one effective control scheme is proposed out to realize predefined-time synchronization based on the results of fixed-time synchronization. Meanwhile, some conditions are given to direct how to select suitable controllers’ parameters by employing fixed-time and Lyapunov theory. The established control schemes are shown to have simpler structures and may need lower energy. In addition, the settling time of fixed time synchronization has been estimated independently on initial states while predefined-time can be set by user in advance. At last, numerical experiments are conducted to verify the effectiveness of the results.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/174618