Finite/fixed-time synchronization of memristive neural networks via event-triggered control

Abstract

By applying event-triggered control schemes, this paper investigates the issues of finite/fixed-time synchronization for a class of memristive neural networks (MNNs). In order to reduce the transmission burden and realize a fast rate of synchronization, the appropriate controller and the measurement error function are designed. Then, by using Lyapunov stability theory and inequality techniques, some efficient criteria are proposed to guarantee the finite/fixed-time synchronization, respectively. Besides, the estimations of the synchronization time are derived, and the Zeno phenomenon can be excluded under the derived event-triggered schemes. Finally, simulations are provided to verify the obtained results.