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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
Zhan, SLam, J
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
0302-9743
2020 SCImago Journal Rankings: 0.249
References
References in Scopus

 

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
dc.identifier.issnl0302-9743-

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