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Article: Global point dissipativity of neural networks with mixed time-varying delays
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TitleGlobal point dissipativity of neural networks with mixed time-varying delays
 
AuthorsCao, J2
Yuan, K2
Ho, DWC3
Lam, J1
 
KeywordsPhysics
 
Issue Date2006
 
PublisherAmerican Institute of Physics. The Journal's web site is located at http://chaos.aip.org/chaos/staff.jsp
 
CitationChaos, 2006, v. 16 n. 1 [How to Cite?]
DOI: http://dx.doi.org/10.1063/1.2126940
 
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.
 
ISSN1054-1500
2012 Impact Factor: 2.188
2012 SCImago Journal Rankings: 0.876
 
DOIhttp://dx.doi.org/10.1063/1.2126940
 
ISI Accession Number IDWOS:000236464500005
 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorCao, J
 
dc.contributor.authorYuan, K
 
dc.contributor.authorHo, DWC
 
dc.contributor.authorLam, J
 
dc.date.accessioned2007-10-30T06:13:56Z
 
dc.date.available2007-10-30T06:13:56Z
 
dc.date.issued2006
 
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.
 
dc.description.naturepublished_or_final_version
 
dc.format.extent191250 bytes
 
dc.format.extent10566 bytes
 
dc.format.mimetypeapplication/pdf
 
dc.format.mimetypetext/plain
 
dc.identifier.citationChaos, 2006, v. 16 n. 1 [How to Cite?]
DOI: http://dx.doi.org/10.1063/1.2126940
 
dc.identifier.doihttp://dx.doi.org/10.1063/1.2126940
 
dc.identifier.hkuros120236
 
dc.identifier.isiWOS:000236464500005
 
dc.identifier.issn1054-1500
2012 Impact Factor: 2.188
2012 SCImago Journal Rankings: 0.876
 
dc.identifier.issue1
 
dc.identifier.openurl
 
dc.identifier.scopuseid_2-s2.0-33745124399
 
dc.identifier.urihttp://hdl.handle.net/10722/44941
 
dc.identifier.volume16
 
dc.languageeng
 
dc.publisherAmerican Institute of Physics. The Journal's web site is located at http://chaos.aip.org/chaos/staff.jsp
 
dc.publisher.placeUnited States
 
dc.relation.ispartofChaos
 
dc.relation.referencesReferences in Scopus
 
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
 
dc.subjectPhysics
 
dc.titleGlobal point dissipativity of neural networks with mixed time-varying delays
 
dc.typeArticle
 
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Author Affiliations
  1. The University of Hong Kong
  2. Southeast University
  3. City University of Hong Kong