Exponential synchronization of memristor-based neural networks with time-varying delay and stochastic perturbation

This paper deals with the stochastic exponential synchronization problem of memristor-based neural networks with time-varying delay. Firstly, considering the state-dependent properties of the memristor, less conservative of model is constructed to analyze the complicated memristor-based neural networks. Then, by applying the stochastic differential inclusions theory and Lyapunov functional approach, sufficient verifiable conditions that depend on the time-varying delay and stochastic perturbation are obtained. It is shown that synchronization can be realized by linear feedback control and adaptive feedback control. The derived results complement and improve the previously known results. Finally, a numerical example is given to illustrate the effectiveness of the theoretical results.