| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 4946774 | Neural Networks | 2016 | 8 Pages |
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.
Related Topics
Physical Sciences and Engineering
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Authors
Zhihong Li, Lei Liu, Quanxin Zhu,