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Article: New stability criteria for neural networks with distributed and probabilistic delays

TitleNew stability criteria for neural networks with distributed and probabilistic delays
Authors
KeywordsDistributed Delay
Exponential Stability
Lyapunov-Krasovskii Functional
Neural Networks
Time-Varying Delay
Issue Date2009
PublisherBirkhaeuser Boston. The Journal's web site is located at http://link.springer.de/link/service/journals/00034/
Citation
Circuits, Systems, And Signal Processing, 2009, v. 28 n. 4, p. 505-522 How to Cite?
AbstractThis paper is concerned with the stability analysis of neural networks with distributed and probabilistic delays. The probabilistic delay satisfies a certain probability distribution. By introducing a stochastic variable with a Bernoulli distribution, the neural network with random time delays is transformed into one with deterministic delays and stochastic parameters. New conditions for the exponential stability of such neural networks are obtained by employing new Lyapunov-Krasovskii functionals and novel techniques for achieving delay dependence. The proposed conditions reduce the conservatism by considering not only the range of the time delays, but also the probability distribution of their variation. A numerical example is provided to show the advantages of the proposed techniques. © Birkhäuser Boston 2008.
Persistent Identifierhttp://hdl.handle.net/10722/157010
ISSN
2015 Impact Factor: 1.178
2015 SCImago Journal Rankings: 0.571
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorYang, Ren_US
dc.contributor.authorGao, Hen_US
dc.contributor.authorLam, Jen_US
dc.contributor.authorShi, Pen_US
dc.date.accessioned2012-08-08T08:44:56Z-
dc.date.available2012-08-08T08:44:56Z-
dc.date.issued2009en_US
dc.identifier.citationCircuits, Systems, And Signal Processing, 2009, v. 28 n. 4, p. 505-522en_US
dc.identifier.issn0278-081Xen_US
dc.identifier.urihttp://hdl.handle.net/10722/157010-
dc.description.abstractThis paper is concerned with the stability analysis of neural networks with distributed and probabilistic delays. The probabilistic delay satisfies a certain probability distribution. By introducing a stochastic variable with a Bernoulli distribution, the neural network with random time delays is transformed into one with deterministic delays and stochastic parameters. New conditions for the exponential stability of such neural networks are obtained by employing new Lyapunov-Krasovskii functionals and novel techniques for achieving delay dependence. The proposed conditions reduce the conservatism by considering not only the range of the time delays, but also the probability distribution of their variation. A numerical example is provided to show the advantages of the proposed techniques. © Birkhäuser Boston 2008.en_US
dc.languageengen_US
dc.publisherBirkhaeuser Boston. The Journal's web site is located at http://link.springer.de/link/service/journals/00034/en_US
dc.relation.ispartofCircuits, Systems, and Signal Processingen_US
dc.subjectDistributed Delayen_US
dc.subjectExponential Stabilityen_US
dc.subjectLyapunov-Krasovskii Functionalen_US
dc.subjectNeural Networksen_US
dc.subjectTime-Varying Delayen_US
dc.titleNew stability criteria for neural networks with distributed and probabilistic delaysen_US
dc.typeArticleen_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.doi10.1007/s00034-008-9092-1en_US
dc.identifier.scopuseid_2-s2.0-67649651953en_US
dc.identifier.hkuros179586-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-67649651953&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume28en_US
dc.identifier.issue4en_US
dc.identifier.spage505en_US
dc.identifier.epage522en_US
dc.identifier.isiWOS:000267146000002-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridYang, R=25928906900en_US
dc.identifier.scopusauthoridGao, H=7402971422en_US
dc.identifier.scopusauthoridLam, J=7201973414en_US
dc.identifier.scopusauthoridShi, P=7202160862en_US
dc.identifier.citeulike3896285-

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