AAM  >> Vol. 3 No. 2 (May 2014)

    Reciprocally Convex Approach to Asymptotic Behavior of Stochastic Hopfield Neural Networks

  • 全文下载: PDF(636KB) HTML    PP.70-77   DOI: 10.12677/AAM.2014.32011  
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Hopfield神经网络有界性均方指数稳定变互式凸组合方法Hopfield Neural Networks Boundedness Mean Square Exponential Stability Reciprocally Convex Approach



In this paper, the problems of stochastic uniformly ultimate boundedness, the existence of stochastic attractor and mean square exponential stability for stochastic Hopfield neural networks are studied by constructing proper Lyapunov-Krasovskii functional, utilizing the reciprocally con- vex approach, the free-weighting matrix method, some stochastic analysis techniques and linear matrix inequalities technique (LMI). We deduce novel results and delay-dependent sufficient conditions for stochastic Hopfield neural networks. Finally, numerical simulations are given on MATLAB to verify the effectiveness of the gained results.

陈无及, 李青松. 随机Hopfield神经网络渐进行为的变互式凸组合方法[J]. 应用数学进展, 2014, 3(2): 70-77. http://dx.doi.org/10.12677/AAM.2014.32011


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