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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, article no. 013105 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
2017 Impact Factor: 2.415
2015 SCImago Journal Rankings: 0.773
ISI Accession Number ID
References

 

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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. 1, article no. 013105-
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.rightsCopyright 2006 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in Chaos, 2006, v. 16 n. 1, article no. 013105 and may be found at https://doi.org/10.1063/1.2126940-
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.spagearticle no. 013105-
dc.identifier.epagearticle no. 013105-
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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