Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorWang, Z-
dc.contributor.authorShu, H-
dc.contributor.authorFang, J-
dc.contributor.authorLiu, X-
dc.identifier.citationNonlinear Analysis: Real World Applications, 7(5): 1119-1128, Dec 2006en_US
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 paper, the asymptotic stability analysis problem is considered for a class of uncertain stochastic neural networks with time delays and parameter uncertainties. The delays are time-invariant, and the uncertainties are norm-bounded that enter into all the network parameters. The aim of this paper is to establish easily verifiable conditions under which the delayed neural network is robustly asymptotically stable in the mean square for all admissible parameter uncertainties. By employing a Lyapunov–Krasovskii functional and conducting the stochastic analysis, a linear matrix inequality (LMI) approach is developed to derive the stability criteria. The proposed criteria can be checked readily by using some standard numerical packages, and no tuning of parameters is required. Examples are provided to demonstrate the effectiveness and applicability of the proposed criteria.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 Germanyen_US
dc.subjectHopfield neural networksen_US
dc.subjectUncertain systemsen_US
dc.subjectStochastic systemsen_US
dc.subjectTime delaysen_US
dc.subjectLyapunov–Krasovskii functionalen_US
dc.subjectGlobal asymptotic stabilityen_US
dc.subjectLinear matrix inequalityen_US
dc.titleRobust stability for stochastic Hopfield neural networks with time delaysen_US
dc.typeResearch Paperen_US
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf150.44 kBAdobe PDFView/Open

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