Finite/fixed-time synchronization of delayed memristive reaction-diffusion neural networks

Wang, S; Guo, Z; Wen, S; Huang, T; Gong, S

Finite/fixed-time synchronization of delayed memristive reaction-diffusion neural networks

Wang, S Guo, Z Wen, S

Huang, T Gong, S

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Neurocomputing, 2020, 375, pp. 1-8
Issue Date:: 2020-01-29

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Field	Value	Language
dc.contributor.author	Wang, S
dc.contributor.author	Guo, Z
dc.contributor.author	Wen, S https://orcid.org/0000-0002-5048-0319
dc.contributor.author	Huang, T
dc.contributor.author	Gong, S
dc.date.accessioned	2021-04-23T05:30:28Z
dc.date.available	2021-04-23T05:30:28Z
dc.date.issued	2020-01-29
dc.identifier.citation	Neurocomputing, 2020, 375, pp. 1-8
dc.identifier.issn	0925-2312
dc.identifier.issn	1872-8286
dc.identifier.uri	http://hdl.handle.net/10453/148317
dc.description.abstract	This paper is concerned with the finite/fixed-time synchronization (FFTS) problems of two delayed memristive reaction–diffusion neural networks (MRDNNs). By designing appropriate state feedback controllers, utilizing the Lyapunov function method and inequality techniques, several sufficient criteria are derived to guarantee the FFTS of the drive-response MRDNNs. Taking into account both the influences of time and space, the model, described as a state-dependent switching system here, is more complex and closer to practical applications than those in the existing results. Finally, an example is presented to substantiate the effectiveness of the theoretical results.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Neurocomputing
dc.relation.isbasedon	10.1016/j.neucom.2019.06.092
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.title	Finite/fixed-time synchronization of delayed memristive reaction-diffusion neural networks
dc.type	Journal Article
utslib.citation.volume	375
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	*
pubs.consider-herdc	false
dc.date.updated	2021-04-23T05:30:26Z
pubs.publication-status	Published
pubs.volume	375

Abstract:

This paper is concerned with the finite/fixed-time synchronization (FFTS) problems of two delayed memristive reaction–diffusion neural networks (MRDNNs). By designing appropriate state feedback controllers, utilizing the Lyapunov function method and inequality techniques, several sufficient criteria are derived to guarantee the FFTS of the drive-response MRDNNs. Taking into account both the influences of time and space, the model, described as a state-dependent switching system here, is more complex and closer to practical applications than those in the existing results. Finally, an example is presented to substantiate the effectiveness of the theoretical results.

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http://hdl.handle.net/10453/148317