Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Full metadata record
DC FieldValueLanguage
dc.contributor.authorZhang, J.-
dc.contributor.authorShi, P.-
dc.contributor.authorQiu, J.-
dc.contributor.authorYang, H.-
dc.identifier.citationMathematical and Computer Modelling, 2008; 47(9-10):1042-1051-
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.publisherPergamon-Elsevier Science Ltd-
dc.rights© 2007 Elsevier Ltd. All rights reserved.-
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-
dc.identifier.orcidShi, P. [0000-0001-8218-586X]-
Appears in Collections:Aurora harvest 4
Electrical and Electronic Engineering publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.