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Article: Passivity criteria for continuous-time neural networks with mixed time-varying delays

TitlePassivity criteria for continuous-time neural networks with mixed time-varying delays
Authors
KeywordsDiscrete Delays
Distributed Delays
Interval Delays
Neural Networks
Passivity
Issue Date2012
PublisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/amc
Citation
Applied Mathematics And Computation, 2012, v. 218 n. 22, p. 11062-11074 How to Cite?
AbstractThis paper is concerned with the problem of passivity analysis for uncertain continuous-time neural networks with mixed time-varying delays. The mixed time-varying delays consist of both discrete and distributed delays, in which the discrete delays are assumed to be varying within a given interval. In addition, the uncertainties are assumed to be norm-bounded. By employing a novel Lyapunov-Krasovskii functional, new passivity delay-interval-dependent criteria are established to guarantee the passivity performance. When estimating an upper bound of the derivative of the Lyapunov-Krasovskii functional, we handle the terms related to the discrete and distributed delays appropriately so as to develop less conservative results. These passivity conditions are presented in terms of linear matrix inequalities, which can be easily solved via standard numerical software. Some numerical examples are given to illustrate the effectiveness of the proposed method. © 2012 Elsevier Inc. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/157201
ISSN
2015 Impact Factor: 1.345
2015 SCImago Journal Rankings: 1.008
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, Hen_US
dc.contributor.authorLam, Jen_US
dc.contributor.authorCheung, KCen_US
dc.date.accessioned2012-08-08T08:45:47Z-
dc.date.available2012-08-08T08:45:47Z-
dc.date.issued2012en_US
dc.identifier.citationApplied Mathematics And Computation, 2012, v. 218 n. 22, p. 11062-11074en_US
dc.identifier.issn0096-3003en_US
dc.identifier.urihttp://hdl.handle.net/10722/157201-
dc.description.abstractThis paper is concerned with the problem of passivity analysis for uncertain continuous-time neural networks with mixed time-varying delays. The mixed time-varying delays consist of both discrete and distributed delays, in which the discrete delays are assumed to be varying within a given interval. In addition, the uncertainties are assumed to be norm-bounded. By employing a novel Lyapunov-Krasovskii functional, new passivity delay-interval-dependent criteria are established to guarantee the passivity performance. When estimating an upper bound of the derivative of the Lyapunov-Krasovskii functional, we handle the terms related to the discrete and distributed delays appropriately so as to develop less conservative results. These passivity conditions are presented in terms of linear matrix inequalities, which can be easily solved via standard numerical software. Some numerical examples are given to illustrate the effectiveness of the proposed method. © 2012 Elsevier Inc. All rights reserved.en_US
dc.languageengen_US
dc.publisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/amcen_US
dc.relation.ispartofApplied Mathematics and Computationen_US
dc.subjectDiscrete Delaysen_US
dc.subjectDistributed Delaysen_US
dc.subjectInterval Delaysen_US
dc.subjectNeural Networksen_US
dc.subjectPassivityen_US
dc.titlePassivity criteria for continuous-time neural networks with mixed time-varying delaysen_US
dc.typeArticleen_US
dc.identifier.emailLam, J:james.lam@hku.hken_US
dc.identifier.authorityLam, J=rp00133en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.amc.2012.05.002en_US
dc.identifier.scopuseid_2-s2.0-84862864158-
dc.identifier.hkuros223448-
dc.identifier.eissn1873-5649-
dc.identifier.isiWOS:000305800700025-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridLi, H=25021832100en_US
dc.identifier.scopusauthoridLam, J=7201973414en_US
dc.identifier.scopusauthoridCheung, KC=36851772500en_US
dc.identifier.citeulike10836667-

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