Passivity analysis of delayed reaction-diffusion memristor-based neural networks.

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

Passivity analysis of delayed reaction-diffusion memristor-based neural networks.

Cao, Y Cao, Y Wen, S

Huang, T Zeng, Z

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Neural Netw, 2019, 109, pp. 159-167
Issue Date:: 2019-01

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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	2022-07-18T05:32:32Z
dc.date.available	2018-10-09
dc.date.available	2022-07-18T05:32:32Z
dc.date.issued	2019-01
dc.identifier.citation	Neural Netw, 2019, 109, pp. 159-167
dc.identifier.issn	0893-6080
dc.identifier.issn	1879-2782
dc.identifier.uri	http://hdl.handle.net/10453/159000
dc.description.abstract	This paper discusses the passivity of delayed reaction-diffusion memristor-based neural networks (RDMNNs). By exploiting inequality techniques and by constructing appropriate Lyapunov functional, several sufficient conditions are obtained in the form of linear matrix inequalities (LMIs), which can be used to ascertain the passivity, output and input strict passivity of delayed RDMNNs. In addition, the passivity of RDMNNs without any delay is also considered. These conditions, represented by LMIs, can be easily verified by virtue of the Matlab toolbox. Finally, some illustrative examples are provided to substantiate the effectiveness and validity of the theoretical results, and to present an application of RDMNN in pseudo-random number generation.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation	National Natural Science Foundation of China61673187
dc.relation.ispartof	Neural Netw
dc.relation.isbasedon	10.1016/j.neunet.2018.10.004
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.mesh	Algorithms
dc.subject.mesh	Diffusion
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Diffusion
dc.subject.mesh	Algorithms
dc.subject.mesh	Neural Networks, Computer
dc.title	Passivity analysis of delayed reaction-diffusion memristor-based neural networks.
dc.type	Journal Article
utslib.citation.volume	109
utslib.location.activity	United States
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	2022-07-18T05:32:24Z
pubs.publication-status	Published
pubs.volume	109

Abstract:

This paper discusses the passivity of delayed reaction-diffusion memristor-based neural networks (RDMNNs). By exploiting inequality techniques and by constructing appropriate Lyapunov functional, several sufficient conditions are obtained in the form of linear matrix inequalities (LMIs), which can be used to ascertain the passivity, output and input strict passivity of delayed RDMNNs. In addition, the passivity of RDMNNs without any delay is also considered. These conditions, represented by LMIs, can be easily verified by virtue of the Matlab toolbox. Finally, some illustrative examples are provided to substantiate the effectiveness and validity of the theoretical results, and to present an application of RDMNN in pseudo-random number generation.

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http://hdl.handle.net/10453/159000