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Delay-Range-Dependent Robust Stability for Uncertain Stochastic Neural Networks with Time-Varying Delays
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Author(s): Wei Feng (Chongqing University and Chongqing Education College, China)and Haixia Wu (Chongqing University and Chongqing Education College, China)
Copyright: 2012
Pages: 15
Source title:
Breakthroughs in Software Science and Computational Intelligence
Source Author(s)/Editor(s): Yingxu Wang (University of Calgary, Canada)
DOI: 10.4018/978-1-4666-0264-9.ch024
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Abstract
This paper is concerned with the robust stability analysis problem for uncertain stochastic neural networks with interval time-varying delays. By utilizing a Lyapunov-Krasovskii functional and conducting stochastic analysis, the authors show that the addressed neural networks are globally, robustly, and asymptotically stable if a convex optimization problem is feasible. Some stability criteria are derived for all admissible uncertainties, and these stability criteria are formulated by means of the feasibility of a linear matrix inequality (LMI), which can be effectively solved by some standard numerical packages. Five numerical examples are given to demonstrate the usefulness of the proposed robust stability criteria.
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