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Conference Paper: Delay-dependent exponential estimates of stochastic neural networks with time delay

TitleDelay-dependent exponential estimates of stochastic neural networks with time delay
Authors
KeywordsExponential Estimates
Linear Matrix Inequalities (Lmis)
Stochastic Neural Networks
Time Delay
Issue Date2006
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2006, v. 4232 LNCS, p. 332-341 How to Cite?
AbstractThis paper is concerned with the exponential estimating problem for a class of stochastic neural networks with time delay. A sufficient condition, which does not only guarantee the exponential stability but also gives the estimates of decay rate and decay coefficient, is established in terms of a new Lyapunov-Krasovskii functional and the linear matrix inequality (LMI) technique. The estimating procedure is implemented by solving a set of LMIs, which can be checked easily by effective algorithms. A numerical example is provided to illustrate the effectiveness of the theoretical results. © Springer-Verlag Berlin Heidelberg 2006.
Persistent Identifierhttp://hdl.handle.net/10722/158963
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorZhan, Sen_US
dc.contributor.authorLam, Jen_US
dc.date.accessioned2012-08-08T09:04:49Z-
dc.date.available2012-08-08T09:04:49Z-
dc.date.issued2006en_US
dc.identifier.citationLecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2006, v. 4232 LNCS, p. 332-341en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/10722/158963-
dc.description.abstractThis paper is concerned with the exponential estimating problem for a class of stochastic neural networks with time delay. A sufficient condition, which does not only guarantee the exponential stability but also gives the estimates of decay rate and decay coefficient, is established in terms of a new Lyapunov-Krasovskii functional and the linear matrix inequality (LMI) technique. The estimating procedure is implemented by solving a set of LMIs, which can be checked easily by effective algorithms. A numerical example is provided to illustrate the effectiveness of the theoretical results. © Springer-Verlag Berlin Heidelberg 2006.en_US
dc.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.subjectExponential Estimatesen_US
dc.subjectLinear Matrix Inequalities (Lmis)en_US
dc.subjectStochastic Neural Networksen_US
dc.subjectTime Delayen_US
dc.titleDelay-dependent exponential estimates of stochastic neural networks with time delayen_US
dc.typeConference_Paperen_US
dc.identifier.emailLam, J:james.lam@hku.hken_US
dc.identifier.authorityLam, J=rp00133en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-33750603763en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33750603763&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume4232 LNCSen_US
dc.identifier.spage332en_US
dc.identifier.epage341en_US
dc.publisher.placeGermanyen_US
dc.identifier.scopusauthoridZhan, S=15052621300en_US
dc.identifier.scopusauthoridLam, J=7201973414en_US

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