Synchronization for Fuzzy Models of Memristive Stochastic Neural Networks via Aperiodically Intermittent Control

In this paper, the synchronization for fuzzy models of memristive neural networks with time delay and stochastic perturbation is investigated. There arc two main differences of this paper with previous relative works: one is that intermittent control is aperiodic while in previous works it's periodic; the other one is that stochastic perturbation is considered in our models. Based on the aperiodically intermittent control scheme, a simple linear feedback controller in the response networks to reach synchronization is designed. Some significant criteria are given to ensure the masterslave synchronization of neural networks. In addition, an adaptive algorithm is designed for the neural networks with time delays. Finally, numerical simulations are exploited to demonstrate the effectiveness of the control strategy.