Asymptotic synchronization for stochastic memristor-based neural networks with noise disturbance

In this paper, globally asymptotical synchronization for stochastic memristor-based neural networks with random noise disturbance is investigated. Under the framework of differential inclusions theory and set-valued maps, a state feedback controller and an adaptive updated law are designed by constructing a suitable Lyapunov functional. By using Itô formula and some significant inequality techniques, sufficient conditions for the global synchronization of the stochastic memristor-based neural networks which are more general are obtained. Finally, numerical simulations are provided to illustrate the theoretical results.