Anti-synchronization of stochastic delayed Cohen-Grossberg neural networks

In this paper, we study the anti-synchronization problem for a class of stochastic delayed Cohen-Grossberg neural networks. By using a Lyapunov-Krasovskii functional, Ito´s formula and stochastic analysis theory, some sufficient conditions are given to ensure that the considered system achieves anti-synchronization. In particular, our results are expresses in term of linear matrix inequalities, which are easy to verify. Finally, a simulation example is provided to show that the suggested method is effective.