Global exponential stability criteria for neural networks with probabilistic delays

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dc.contributor.authorMahmoud, M.
dc.contributor.authorSelim, S.
dc.contributor.authorShi, P.
dc.date.issued2010
dc.description.abstractThe problem of global exponential stability analysis for a class of neural networks (NNs) with probabilistic delays is discussed in this paper. The delay is assumed to follow a given probability density function. This function is discretised into arbitrary number of intervals. In this way, the NN with random time delays is transformed into one with deterministic delays and random parameters. New conditions for the exponential stability of such NNs are obtained by employing new Lyapunov-Krasovskii functionals and novel techniques for achieving delay dependence. It is established that these conditions reduce the conservatism by considering not only the range of the time delays, but also the probability distribution of their variation. Numerical examples are provided to show the advantages of the proposed techniques.
dc.description.statementofresponsibilityM.S. Mahmoud, S.Z. Selim, P. Shi
dc.identifier.citationIET Control Theory and Applications, 2010; 4(11):2405-2415
dc.identifier.doi10.1049/iet-cta.2009.0007
dc.identifier.issn1751-8644
dc.identifier.issn1751-8652
dc.identifier.orcidShi, P. [0000-0001-6295-0405] [0000-0001-8218-586X] [0000-0002-0864-552X] [0000-0002-1358-2367] [0000-0002-5312-5435]
dc.identifier.urihttp://hdl.handle.net/2440/86261
dc.language.isoen
dc.publisherInstitution of Engineering and Technology
dc.rights©The Institution of Engineering and Technology 2010
dc.source.urihttps://doi.org/10.1049/iet-cta.2009.0007
dc.titleGlobal exponential stability criteria for neural networks with probabilistic delays
dc.typeJournal article
pubs.publication-statusPublished

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