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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
Issue 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.
ISSN: 1468-1218
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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