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Article: A new criterion of delay-dependent asymptotic stability for Hopfield neural networks with time delay

TitleA new criterion of delay-dependent asymptotic stability for Hopfield neural networks with time delay
Authors
KeywordsGlobal asymptotic stability
Hopfield neural network (HNN)
Linear matrix inequality (LMI)
Lyapunov functional
Issue Date2008
PublisherIEEE.
Citation
IEEE Transactions On Neural Networks, 2008, v. 19 n. 3, p. 532-535 How to Cite?
AbstractIn this brief, the problem of global asymptotic stability for delayed Hopfield neural networks (HNNs) is investigated. A new criterion of asymptotic stability is derived by introducing a new kind of Lyapunov-Krasovskii functional and is formulated in terms of a linear matrix inequality (LMI), which can be readily solved via standard software. This new criterion based on a delay fractioning approach proves to be much less conservative and the conservatism could be notably reduced by thinning the delay fractioning. An example is provided to show the effectiveness and the advantage of the proposed result. © 2008 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/57195
ISSN
2011 Impact Factor: 2.952
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorMou, Sen_HK
dc.contributor.authorGao, Hen_HK
dc.contributor.authorLam, Jen_HK
dc.contributor.authorQiang, Wen_HK
dc.date.accessioned2010-04-12T01:29:08Z-
dc.date.available2010-04-12T01:29:08Z-
dc.date.issued2008en_HK
dc.identifier.citationIEEE Transactions On Neural Networks, 2008, v. 19 n. 3, p. 532-535en_HK
dc.identifier.issn1045-9227en_HK
dc.identifier.urihttp://hdl.handle.net/10722/57195-
dc.description.abstractIn this brief, the problem of global asymptotic stability for delayed Hopfield neural networks (HNNs) is investigated. A new criterion of asymptotic stability is derived by introducing a new kind of Lyapunov-Krasovskii functional and is formulated in terms of a linear matrix inequality (LMI), which can be readily solved via standard software. This new criterion based on a delay fractioning approach proves to be much less conservative and the conservatism could be notably reduced by thinning the delay fractioning. An example is provided to show the effectiveness and the advantage of the proposed result. © 2008 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Transactions on Neural Networksen_HK
dc.rights©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectGlobal asymptotic stabilityen_HK
dc.subjectHopfield neural network (HNN)en_HK
dc.subjectLinear matrix inequality (LMI)en_HK
dc.subjectLyapunov functionalen_HK
dc.subject.meshAlgorithmsen_HK
dc.subject.meshHumansen_HK
dc.subject.meshNeural Networks (Computer)en_HK
dc.subject.meshSignal Processing, Computer-Assisteden_HK
dc.subject.meshTime Factorsen_HK
dc.titleA new criterion of delay-dependent asymptotic stability for Hopfield neural networks with time delayen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1045-9227&volume=19&issue=3&spage=532&epage=535&date=2008&atitle=A+new+criterion+of+delay-dependent+asymptotic+stability+for+hopfield+neural+networks+with+time+delayen_HK
dc.identifier.emailLam, J:james.lam@hku.hken_HK
dc.identifier.authorityLam, J=rp00133en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/TNN.2007.912593en_HK
dc.identifier.pmid18334372-
dc.identifier.scopuseid_2-s2.0-40949160176en_HK
dc.identifier.hkuros150048-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-40949160176&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume19en_HK
dc.identifier.issue3en_HK
dc.identifier.spage532en_HK
dc.identifier.epage535en_HK
dc.identifier.isiWOS:000253692800014-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridMou, S=16177795900en_HK
dc.identifier.scopusauthoridGao, H=7402971422en_HK
dc.identifier.scopusauthoridLam, J=7201973414en_HK
dc.identifier.scopusauthoridQiang, W=7007041991en_HK

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