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dc.contributor.authorYang, R.-
dc.contributor.authorGao, H.-
dc.contributor.authorLam, J.-
dc.contributor.authorShi, P.-
dc.identifier.citationCircuits, Systems and Signal Processing, 2009; 28(4):505-522-
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.-
dc.description.statementofresponsibilityRongni Yang, Huijun Gao, James Lam, Peng Shi-
dc.publisherSP Birkhäuser Verlag Boston-
dc.rights© Birkhäuser Boston 2008-
dc.subjectDistributed delay-
dc.subjectExponential stability-
dc.subjectNeural networks-
dc.subjectLyapunov–Krasovskii functional-
dc.subjectTime-varying delay-
dc.titleNew stability criteria for neural networks with distributed and probabilistic delays-
dc.typeJournal article-
dc.identifier.orcidShi, P. [0000-0001-8218-586X]-
Appears in Collections:Aurora harvest 2
Electrical and Electronic Engineering publications

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