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Article: Global point dissipativity of neural networks with mixed time-varying delays

TitleGlobal point dissipativity of neural networks with mixed time-varying delays
Authors
KeywordsPhysics
Issue Date2006
PublisherAmerican Institute of Physics. The Journal's web site is located at http://chaos.aip.org/chaos/staff.jsp
Citation
Chaos, 2006, v. 16 n. 1 How to Cite?
AbstractBy employing the Lyapunov method and some inequality techniques, the global point dissipativity is studied for neural networks with both discrete time-varying delays and distributed time-varying delays. Simple sufficient conditions are given for checking the global point dissipativity of neural networks with mixed time-varying delays. The proposed linear matrix inequality approach is computationally efficient as it can be solved numerically using standard commercial software. Illustrated examples are given to show the usefulness of the results in comparison with some existing results. © 2006 American Institute of Physics.
Persistent Identifierhttp://hdl.handle.net/10722/44941
ISSN
2014 Impact Factor: 1.954
2014 SCImago Journal Rankings: 0.775
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorCao, Jen_HK
dc.contributor.authorYuan, Ken_HK
dc.contributor.authorHo, DWCen_HK
dc.contributor.authorLam, Jen_HK
dc.date.accessioned2007-10-30T06:13:56Z-
dc.date.available2007-10-30T06:13:56Z-
dc.date.issued2006en_HK
dc.identifier.citationChaos, 2006, v. 16 n. 1en_HK
dc.identifier.issn1054-1500en_HK
dc.identifier.urihttp://hdl.handle.net/10722/44941-
dc.description.abstractBy employing the Lyapunov method and some inequality techniques, the global point dissipativity is studied for neural networks with both discrete time-varying delays and distributed time-varying delays. Simple sufficient conditions are given for checking the global point dissipativity of neural networks with mixed time-varying delays. The proposed linear matrix inequality approach is computationally efficient as it can be solved numerically using standard commercial software. Illustrated examples are given to show the usefulness of the results in comparison with some existing results. © 2006 American Institute of Physics.en_HK
dc.format.extent191250 bytes-
dc.format.extent10566 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherAmerican Institute of Physics. The Journal's web site is located at http://chaos.aip.org/chaos/staff.jspen_HK
dc.relation.ispartofChaosen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectPhysicsen_HK
dc.titleGlobal point dissipativity of neural networks with mixed time-varying delaysen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1054-1500&volume=16&issue=1&spage=013105:1&epage=9&date=2006&atitle=Global+point+dissipativity+of+neural+networks+with+mixed+time-varying+delaysen_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.doi10.1063/1.2126940en_HK
dc.identifier.scopuseid_2-s2.0-33745124399en_HK
dc.identifier.hkuros120236-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33745124399&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume16en_HK
dc.identifier.issue1en_HK
dc.identifier.isiWOS:000236464500005-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridCao, J=7403354075en_HK
dc.identifier.scopusauthoridYuan, K=8857236000en_HK
dc.identifier.scopusauthoridHo, DWC=7402971938en_HK
dc.identifier.scopusauthoridLam, J=7201973414en_HK

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