Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/4948
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dc.contributor.authorWang, Z-
dc.contributor.authorLiu, Y-
dc.contributor.authorYu, L-
dc.contributor.authorIu, X-
dc.date.accessioned2011-04-04T10:34:19Z-
dc.date.available2011-04-04T10:34:19Z-
dc.date.issued2006-
dc.identifier.citationPhysics Letters A, 356(4-5): 346-352, Aug 2006en_US
dc.identifier.issn0375-9601-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/4948-
dc.descriptionThis is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.en_US
dc.description.abstractIn this Letter, the global exponential stability analysis problem is considered for a class of recurrent neural networks (RNNs) with time delays and Markovian jumping parameters. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite state space. The purpose of the problem addressed is to derive some easy-to-test conditions such that the dynamics of the neural network is stochastically exponentially stable in the mean square, independent of the time delay. By employing a new Lyapunov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish the desired sufficient conditions, and therefore the global exponential stability in the mean square for the delayed RNNs can be easily checked by utilizing the numerically efficient Matlab LMI toolbox, and no tuning of parameters is required. A numerical example is exploited to show the usefulness of the derived LMI-based stability conditions.en_US
dc.description.sponsorshipThis work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, and the Alexander von Humboldt Foundation of Germany.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectRecurrent neural networksen_US
dc.subjectMarkovian jumping parametersen_US
dc.subjectTime delaysen_US
dc.subjectStochastic systemsen_US
dc.subjectGlobal exponential stability in the mean squareen_US
dc.subjectLinear matrix inequalityen_US
dc.titleExponential stability of delayed recurrent neural networks with Markovian jumping parametersen_US
dc.typeResearch Paperen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.physleta.2006.03.078-
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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