Mean-square exponential input-to-state stability of stochastic fuzzy delayed Cohen-Grossberg neural networks

We consider a class of stochastic fuzzy delayed Cohen-Grossberg neural networks without global Lipschitz condition. Based on local Lipschitz condition, we prove the solutions of the given neural networks exist globally and are mean-square exponentially input-to-state stable. Moreover, we highlight the advantages of our novel results by comparing with the results in Zhu and Li (2012) as well as a numerical example.