Event-based passification of delayed memristive neural networks

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

Event-based passification of delayed memristive neural networks

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

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Information Sciences, 2021, 569, pp. 344-357
Issue Date:: 2021-08-01

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Field	Value	Language
dc.contributor.author	Cao, Y
dc.contributor.author	Wang, S
dc.contributor.author	Guo, Z
dc.contributor.author	Huang, T
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.date.accessioned	2022-05-31T23:58:13Z
dc.date.available	2022-05-31T23:58:13Z
dc.date.issued	2021-08-01
dc.identifier.citation	Information Sciences, 2021, 569, pp. 344-357
dc.identifier.issn	0020-0255
dc.identifier.uri	http://hdl.handle.net/10453/157851
dc.description.abstract	This paper focuses on the passification issue of delayed memristive neural networks via the event-based control. First, by designing an appropriate controller based on a static event trigger scheme, the passification conditions are deduced for delayed memristive neural networks. Then, under the same controller, the passivity is discussed for the delayed memristive neural network system with a more economical and realistic dynamic event trigger rule. Meanwhile, in order to ensure these two event trigger control schemes are Zeno free, the existence of positive lower bounds are approved for the inter event time. Finally, illustrative examples are elaborated to support the theoretical results.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Information Sciences
dc.relation.isbasedon	10.1016/j.ins.2021.03.045
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	Event-based passification of delayed memristive neural networks
dc.type	Journal Article
utslib.citation.volume	569
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	2022-05-31T23:58:11Z
pubs.publication-status	Published
pubs.volume	569

Abstract:

This paper focuses on the passification issue of delayed memristive neural networks via the event-based control. First, by designing an appropriate controller based on a static event trigger scheme, the passification conditions are deduced for delayed memristive neural networks. Then, under the same controller, the passivity is discussed for the delayed memristive neural network system with a more economical and realistic dynamic event trigger rule. Meanwhile, in order to ensure these two event trigger control schemes are Zeno free, the existence of positive lower bounds are approved for the inter event time. Finally, illustrative examples are elaborated to support the theoretical results.

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http://hdl.handle.net/10453/157851