Passivity and passification of memristive neural networks with leakage term and time-varying delays

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

Passivity and passification of memristive neural networks with leakage term and time-varying delays

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

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Applied Mathematics and Computation, 2019, 361, pp. 294-310
Issue Date:: 2019-11-15

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Field	Value	Language
dc.contributor.author	Wang, S
dc.contributor.author	Cao, Y
dc.contributor.author	Huang, T
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.date.accessioned	2023-03-13T12:33:25Z
dc.date.available	2023-03-13T12:33:25Z
dc.date.issued	2019-11-15
dc.identifier.citation	Applied Mathematics and Computation, 2019, 361, pp. 294-310
dc.identifier.issn	0096-3003
dc.identifier.issn	1873-5649
dc.identifier.uri	http://hdl.handle.net/10453/167164
dc.description.abstract	This paper investigates passivity and passification for memristive neural networks (MNNs) with both leakage and time-varying delays. MNNs are converted into traditional neural networks (NNs) by nonsmooth analysis, then sufficient conditions are derived to guarantee the passivity based on Lyapunov method. A novel Lyapunov–Krasovskii functional (LKF) is constructed without requiring all the symmetric matrices to be positive definite. The relaxed passivity criteria with less conservativeness or complexity are obtained in the form of linear matrix inequalities (LMIs), which can be verified easily by the LMI toolbox. Then, the passification controller is designed with the relaxed criteria to ensure that MNNs with both leakage and time-varying delays are passive. Finally, two pertinent examples are presented to show the effectiveness of the theoretical results.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Applied Mathematics and Computation
dc.relation.isbasedon	10.1016/j.amc.2019.05.040
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	Passivity and passification of memristive neural networks with leakage term and time-varying delays
dc.type	Journal Article
utslib.citation.volume	361
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	2023-03-13T12:33:24Z
pubs.publication-status	Published
pubs.volume	361

Abstract:

This paper investigates passivity and passification for memristive neural networks (MNNs) with both leakage and time-varying delays. MNNs are converted into traditional neural networks (NNs) by nonsmooth analysis, then sufficient conditions are derived to guarantee the passivity based on Lyapunov method. A novel Lyapunov–Krasovskii functional (LKF) is constructed without requiring all the symmetric matrices to be positive definite. The relaxed passivity criteria with less conservativeness or complexity are obtained in the form of linear matrix inequalities (LMIs), which can be verified easily by the LMI toolbox. Then, the passification controller is designed with the relaxed criteria to ensure that MNNs with both leakage and time-varying delays are passive. Finally, two pertinent examples are presented to show the effectiveness of the theoretical results.

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http://hdl.handle.net/10453/167164