Global Stability for Stochastic Interval Neural Networks with Mixed Time Delays
The globally stochastically asymptotic stability is considered for a class of stochastic interval neural networks with mixed delays (SIDNN) in this paper, which consist of both the discrete and distributed time delays. By introducing an equivalent transformation of interval matrices, a criterion on globally stochastically asymptotic stability is established. Based on an Lyapunov function and stochastic analysis theory, a linear matrix (LMI) approach is developed to derive the sufficient condition guaranteeing the globally stochastically asymptotic stability of the equilibrium point, where the feasibility of LMIs can be easily solved by the LMI-Toolbox in Matlab, and all the delayed differential equations are calculated numerically via DDE23 Program in Matlab.
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This work was supported by Shanxi Province Science Foundation for Youths,China(No. 2010JQ1016), the Science Research Foundation of Shaanxi Province Department of Education, China(No. 2010JK560).
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