Synchronization for fractional-order time-delayed memristor-based neural networks with parameter uncertainty

In this paper, the global synchronization for fractional-order multiple time-delayed memristor-based neural networks with parameter uncertainty is investigated. A comparison theorem for a class of fractional-order multiple time-delayed systems is given. Under the framework of Filippov solution and differential inclusion theory, the synchronization conditions of fractional-order multiple time-delayed memristor-based neural networks with parameter uncertainty are derived, based on the given comparison theorem and Lyapunov method. Furthermore, the global asymptotical stability conditions of fractional-order multiple time-delayed memristor-based neural networks are obtained. Finally, two numerical examples are presented to show the effectiveness of our theoretical results.