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Title: Stochastic stability of uncertain Hopfield neural networks with discrete and distributed delays
Authors: Wang, Z
Liu, Y
Fraser, K
Liu, X
Keywords: Hopfield neural networks
Uncertain systems
Stochastic systems
Distributed delays
Discrete delays
Lyapunov–Krasovskii functional
Global asymptotic stability
Linear matrix inequality
Publication Date: 2006
Publisher: Elsevier
Citation: Physics Letters A, 354(4): 288-297, Jun 2006
Abstract: This Letter is concerned with the global asymptotic stability analysis problem for a class of uncertain stochastic Hopfield neural networks with discrete and distributed time-delays. By utilizing a Lyapunov–Krasovskii functional, using the well-known S-procedure and conducting stochastic analysis, we show that the addressed neural networks are robustly, globally, asymptotically stable if a convex optimization problem is feasible. Then, the stability criteria are derived in terms of linear matrix inequalities (LMIs), which can be effectively solved by some standard numerical packages. The main results are also extended to the multiple time-delay case. Two numerical examples are given to demonstrate the usefulness of the proposed global stability condition.
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.
ISSN: 0375-9601
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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