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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/4947

Title: Robust stability for stochastic Hopfield neural networks with time delays
Authors: Wang, Z
Shu, H
Fang, J
Liu, X
Keywords: Hopfield neural networks
Uncertain systems
Stochastic systems
Time delays
Lyapunov–Krasovskii functional
Global asymptotic stability
Linear matrix inequality
Publication Date: 2006
Publisher: Elsevier
Citation: Nonlinear Analysis: Real World Applications, 7(5): 1119-1128, Dec 2006
Abstract: In 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.
Description: This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.
Sponsorship: This 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
URI: http://bura.brunel.ac.uk/handle/2438/4947
DOI: http://dx.doi.org/10.1016/j.nonrwa.2005.10.004
ISSN: 1468-1218
Appears in Collections:School of Information Systems, Computing and Mathematics Research Papers
Computer Science

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