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

File Download Links for fulltext
(May Require Subscription)
Supplementary
  • Basic View
  • Metadata View
  • XML View
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
2011 Impact Factor: 2.076
2011 SCImago Journal Rankings: 0.094
DOIhttp://dx.doi.org/10.1063/1.2126940
ISI Accession Number IDWOS:000236464500005
ReferencesReferences in Scopus
DC Field
Value
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
2011 Impact Factor: 2.076
2011 SCImago Journal Rankings: 0.094
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
Author Affiliations
  1. The University of Hong Kong
  2. Southeast University
  3. City University of Hong Kong