Event-based sliding-mode synchronization of delayed memristive neural networks via continuous/periodic sampling algorithm

Wang, Y; Cao, Y; Guo, Z; Huang, T; Wen, S

Event-based sliding-mode synchronization of delayed memristive neural networks via continuous/periodic sampling algorithm

Wang, Y Cao, Y Guo, Z Huang, T Wen, S

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Publisher:: ELSEVIER SCIENCE INC
Publication Type:: Journal Article
Citation:: Applied Mathematics and Computation, 2020, 383
Issue Date:: 2020-10-15

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Field	Value	Language
dc.contributor.author	Wang, Y
dc.contributor.author	Cao, Y
dc.contributor.author	Guo, Z
dc.contributor.author	Huang, T
dc.contributor.author	Wen, S https://orcid.org/0000-0002-5048-0319
dc.date.accessioned	2021-04-23T05:15:31Z
dc.date.available	2021-04-23T05:15:31Z
dc.date.issued	2020-10-15
dc.identifier.citation	Applied Mathematics and Computation, 2020, 383
dc.identifier.issn	0096-3003
dc.identifier.issn	1873-5649
dc.identifier.uri	http://hdl.handle.net/10453/148314
dc.description.abstract	This paper investigates the problem of event-based sliding-mode synchronization of memristive neural networks with delay through continuous/periodic sampling algorithm. Memristive neural networks are converted into the form of general neural networks by nonsmooth analysis. Then the controller is designed on the sliding surface selected and the trajectory of the system with this controller are analyzed in detail. Based on the continuous sampling, this paper further draws new results with the periodic sampling rule. Finally, some numerical examples are given to verify the correctness of the theoretical results.
dc.language	English
dc.publisher	ELSEVIER SCIENCE INC
dc.relation.ispartof	Applied Mathematics and Computation
dc.relation.isbasedon	10.1016/j.amc.2020.125379
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0103 Numerical and Computational Mathematics, 0802 Computation Theory and Mathematics
dc.subject.classification	Numerical & Computational Mathematics
dc.title	Event-based sliding-mode synchronization of delayed memristive neural networks via continuous/periodic sampling algorithm
dc.type	Journal Article
utslib.citation.volume	383
utslib.for	0102 Applied Mathematics
utslib.for	0103 Numerical and Computational Mathematics
utslib.for	0802 Computation Theory and Mathematics
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	2021-04-23T05:15:29Z
pubs.publication-status	Published
pubs.volume	383

Abstract:

This paper investigates the problem of event-based sliding-mode synchronization of memristive neural networks with delay through continuous/periodic sampling algorithm. Memristive neural networks are converted into the form of general neural networks by nonsmooth analysis. Then the controller is designed on the sliding surface selected and the trajectory of the system with this controller are analyzed in detail. Based on the continuous sampling, this paper further draws new results with the periodic sampling rule. Finally, some numerical examples are given to verify the correctness of the theoretical results.

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http://hdl.handle.net/10453/148314