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Article: Global exponential estimates of stochastic interval neural networks with discrete and distributed delays

TitleGlobal exponential estimates of stochastic interval neural networks with discrete and distributed delays
Authors
KeywordsDiscrete delay
Distributed delay
Exponential estimates
Interval systems
Linear matrix inequalities (LMIs)
Stochastic neural networks
Issue Date2008
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/neucom
Citation
Neurocomputing, 2008, v. 71 n. 13-15, p. 2950-2963 How to Cite?
AbstractThis paper is concerned with the robust exponential estimating problem for a class of neural networks with discrete and distributed delays. The considered neural networks are disturbed by Wiener processes, and possess interval uncertainties in the system parameters. A sufficient condition, which does not only guarantee the global exponential stability but also provides more exact characterizations on the decay rate and the coefficient, is established in terms of a novel Lyapunov-Krasovskii functional equipped with appropriately constructed exponential terms and the linear matrix inequality (LMI) technique. The estimates of the decay rate and the coefficient are obtained by solving a set of LMIs, which can be implemented easily by effective algorithms. In addition, slack matrices are introduced to reduce the conservatism of the condition. A numerical example is provided to illustrate the effectiveness of the theoretical results. © 2007 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/59106
ISSN
2015 Impact Factor: 2.392
2015 SCImago Journal Rankings: 1.202
ISI Accession Number ID
Funding AgencyGrant Number
RGCHKU 7031/06P
Funding Information:

This work was partially supported by RGC HKU 7031/06P.

References
Grants

 

DC FieldValueLanguage
dc.contributor.authorShu, Zen_HK
dc.contributor.authorLam, Jen_HK
dc.date.accessioned2010-05-31T03:42:58Z-
dc.date.available2010-05-31T03:42:58Z-
dc.date.issued2008en_HK
dc.identifier.citationNeurocomputing, 2008, v. 71 n. 13-15, p. 2950-2963en_HK
dc.identifier.issn0925-2312en_HK
dc.identifier.urihttp://hdl.handle.net/10722/59106-
dc.description.abstractThis paper is concerned with the robust exponential estimating problem for a class of neural networks with discrete and distributed delays. The considered neural networks are disturbed by Wiener processes, and possess interval uncertainties in the system parameters. A sufficient condition, which does not only guarantee the global exponential stability but also provides more exact characterizations on the decay rate and the coefficient, is established in terms of a novel Lyapunov-Krasovskii functional equipped with appropriately constructed exponential terms and the linear matrix inequality (LMI) technique. The estimates of the decay rate and the coefficient are obtained by solving a set of LMIs, which can be implemented easily by effective algorithms. In addition, slack matrices are introduced to reduce the conservatism of the condition. A numerical example is provided to illustrate the effectiveness of the theoretical results. © 2007 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/neucomen_HK
dc.relation.ispartofNeurocomputingen_HK
dc.rightsNeurocomputing. Copyright © Elsevier BV.en_HK
dc.subjectDiscrete delayen_HK
dc.subjectDistributed delayen_HK
dc.subjectExponential estimatesen_HK
dc.subjectInterval systemsen_HK
dc.subjectLinear matrix inequalities (LMIs)en_HK
dc.subjectStochastic neural networksen_HK
dc.titleGlobal exponential estimates of stochastic interval neural networks with discrete and distributed delaysen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0925-2312&volume=71&issue=13-15&spage=2950&epage=2963&date=2008&atitle=Global+exponential+estimates+of+stochastic+interval+neural+networks+with+discrete+and+distributed+delaysen_HK
dc.identifier.emailLam, J:james.lam@hku.hken_HK
dc.identifier.authorityLam, J=rp00133en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.neucom.2007.07.003en_HK
dc.identifier.scopuseid_2-s2.0-56449125024en_HK
dc.identifier.hkuros164139en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-56449125024&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume71en_HK
dc.identifier.issue13-15en_HK
dc.identifier.spage2950en_HK
dc.identifier.epage2963en_HK
dc.identifier.isiWOS:000259121100054-
dc.publisher.placeNetherlandsen_HK
dc.relation.projectDecay rate estimation and synthesis of functional differential systems via semi-definite programming-
dc.identifier.scopusauthoridShu, Z=25652150400en_HK
dc.identifier.scopusauthoridLam, J=7201973414en_HK

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