Exponential Input-to-State Stability of Stochastic Fuzzy Delayed Cohen-Grossberg Neural Networks Under Impulsive Perturbations

In this paper, we study a class of stochastic fuzzy delayed Cohen-Grossberg neural networks under impulsive perturbations. By Razumikhin method and iteration technique, some sufficient conditions ensuring the mean-square exponential input-to-state stability of the designed delayed neural networks are obtained. These results can generalize and improve some earlier publications. A numerical example is demonstrated to verify the theoretical results.