基于向量李雅普诺夫函数的具有马尔可夫切换的时滞 Cohen-Grossberg 神经网络的均方指数输入状态稳定性。
Mean-square exponential input-to-state stability of delayed Cohen-Grossberg neural networks with Markovian switching based on vector Lyapunov functions.
机构信息
College of Science, Hohai University, Nanjing, 210098, China.
School of Mathematical Sciences and Institute of Finance and Statistics, Nanjing Normal University, Nanjing, 210023, China; Department of Mathematics, University of Bielefeld, Bielefeld D-33615, Germany.
出版信息
Neural Netw. 2016 Dec;84:39-46. doi: 10.1016/j.neunet.2016.08.001. Epub 2016 Aug 24.
This paper studies the mean-square exponential input-to-state stability of delayed Cohen-Grossberg neural networks with Markovian switching. By using the vector Lyapunov function and property of M-matrix, two generalized Halanay inequalities are established. By means of the generalized Halanay inequalities, sufficient conditions are also obtained, which can ensure the exponential input-to-state stability of delayed Cohen-Grossberg neural networks with Markovian switching. Two numerical examples are given to illustrate the efficiency of the derived results.
本文研究了具有马尔可夫切换的时滞 Cohen-Grossberg 神经网络的均方指数输入状态稳定性。通过使用向量 Lyapunov 函数和 M-矩阵的性质，建立了两个广义 Halanay 不等式。利用广义 Halanay 不等式，得到了确保具有马尔可夫切换的时滞 Cohen-Grossberg 神经网络指数输入状态稳定性的充分条件。给出了两个数值示例来说明所得到结果的有效性。
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