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.
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。
