A new criterion for exponential stability of uncertain stochastic neural networks with mixed delays

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dc.contributor.authorZhang, J.
dc.contributor.authorShi, P.
dc.contributor.authorQiu, J.
dc.contributor.authorYang, H.
dc.date.issued2008
dc.description.abstractThis paper deals with the problem of exponential stability for a class of uncertain stochastic neural networks with both discrete and distributed delays (also called mixed delays). The system possesses time-varying and norm-bounded uncertainties. Based on Lyapunov–Krasovskii functional and stochastic analysis approaches, new stability criteria are presented in terms of linear matrix inequalities to guarantee the delayed neural networks to be robustly exponentially stable in the mean square for all admissible parameter uncertainties. Numerical examples are given to illustrate the effectiveness of the developed techniques.
dc.description.statementofresponsibilityJinhui Zhang, Peng Shi, Jiqing Qiu, Hongjiu Yang
dc.identifier.citationMathematical and Computer Modelling, 2008; 47(9-10):1042-1051
dc.identifier.doi10.1016/j.mcm.2007.05.014
dc.identifier.issn0895-7177
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/2440/83496
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.rights© 2007 Elsevier Ltd. All rights reserved.
dc.source.urihttps://doi.org/10.1016/j.mcm.2007.05.014
dc.subjectStochastic neural networks
dc.subjectTime delays
dc.subjectExponential stability
dc.subjectLinear matrix inequalities (LMIs)
dc.subjectNorm-bounded uncertainties
dc.titleA new criterion for exponential stability of uncertain stochastic neural networks with mixed delays
dc.typeJournal article
pubs.publication-statusPublished

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