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Title: On global asymptotic stability of neural networks with discrete and distributed delays
Authors: Wang, Z
Liu, Y
Liu, X
Keywords: Neural networks
Distributed delays
Discrete delays
Lyapunov–Krasovskii functional
Global asymptotic stability
Linear matrix inequality
Publication Date: 2005
Publisher: Elsevier
Citation: Physics Letters A, 345(4-6): 299-308, Oct 2005
Abstract: In this Letter, the global asymptotic stability analysis problem is investigated for a class of neural networks with discrete and distributed time-delays. The purpose of the problem is to determine the asymptotic stability by employing some easy-to-test conditions. It is shown, via the Lyapunov–Krasovskii stability theory, that the class of neural networks under consideration is globally asymptotically stable if a quadratic matrix inequality involving several parameters is feasible. Furthermore, a linear matrix inequality (LMI) approach is exploited to transform the addressed stability analysis problem into a convex optimization problem, and sufficient conditions for the neural networks to be globally asymptotically stable are then derived in terms of a linear matrix inequality, which can be readily solved by using the Matlab LMI toolbox. Two numerical examples are provided to show 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 2005 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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