Passivity analysis of coupled neural networks with reaction–diffusion terms and mixed delays

Cao, Y; Cao, Y; Wen, S; Huang, T; Zeng, Z

Passivity analysis of coupled neural networks with reaction–diffusion terms and mixed delays

Cao, Y Cao, Y Wen, S

Huang, T Zeng, Z

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Journal of the Franklin Institute, 2018, 355, (17), pp. 8915-8933
Issue Date:: 2018-11-01

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Field	Value	Language
dc.contributor.author	Cao, Y
dc.contributor.author	Cao, Y
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Huang, T
dc.contributor.author	Zeng, Z
dc.date.accessioned	2022-09-04T01:24:41Z
dc.date.available	2022-09-04T01:24:41Z
dc.date.issued	2018-11-01
dc.identifier.citation	Journal of the Franklin Institute, 2018, 355, (17), pp. 8915-8933
dc.identifier.issn	0016-0032
dc.identifier.issn	1879-2693
dc.identifier.uri	http://hdl.handle.net/10453/161308
dc.description.abstract	In this paper, we intend to discuss the passivity of coupled neural networks (NNs) with reaction–diffusion terms and mixed delays. By constructing appropriate Lyapunov functional, and with the help of liner matrix inequalities, some inequality techniques, several sufficient conditions are derived to guarantee the output strictly passive, input strictly passive, passive of the proposed neural network model. Then, a stability criterion is presented according to the obtained passivity results. Moreover, the proposed neural network model herein is more general than some recent studies, which can improve and enrich the previous research results. Finally, a numerical example is presented to show the effectiveness of the theoretical criteria.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation	National Natural Science Foundation of China61673187
dc.relation.ispartof	Journal of the Franklin Institute
dc.relation.isbasedon	10.1016/j.jfranklin.2018.09.030
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0906 Electrical and Electronic Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.title	Passivity analysis of coupled neural networks with reaction–diffusion terms and mixed delays
dc.type	Journal Article
utslib.citation.volume	355
utslib.for	0102 Applied Mathematics
utslib.for	0906 Electrical and Electronic 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	2022-09-04T01:24:40Z
pubs.issue	17
pubs.publication-status	Published
pubs.volume	355
utslib.citation.issue	17

Abstract:

In this paper, we intend to discuss the passivity of coupled neural networks (NNs) with reaction–diffusion terms and mixed delays. By constructing appropriate Lyapunov functional, and with the help of liner matrix inequalities, some inequality techniques, several sufficient conditions are derived to guarantee the output strictly passive, input strictly passive, passive of the proposed neural network model. Then, a stability criterion is presented according to the obtained passivity results. Moreover, the proposed neural network model herein is more general than some recent studies, which can improve and enrich the previous research results. Finally, a numerical example is presented to show the effectiveness of the theoretical criteria.

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http://hdl.handle.net/10453/161308