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Mean-square exponential input-to-state stability of delayed Cohen-Grossberg neural networks with Markovian switching
Zhihong Li1, Lei Liu1, Quanxin Zhu2
1College of Science, Hohai University, Nanjing, 210098, China.
Abstract:
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.
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