Average Impulsive Weight Based Event-Triggered Impulsive Synchronization on Coupled Neural Networks

Abstract

This article explores the stochastic synchronization for a class of coupled neural networks through a novel event-triggered impulsive control strategy. In view of hybrid impulses in the controller, the desynchronizing and synchronizing impulses are both discussed in consideration of the average impulsive weight. The triggering conditions are presented according to the latest impulsive weight and the overall impulsive weight, respectively and an exponential threshold function is correspondingly proposed to explain the triggering mechanism. Sufficient conditions for the synchronization are successfully obtained with jointly utilizing the mathematical induction methodology and the variation of parameter formula. In addition, the convergence velocity of the coupled neural networks is precisely estimated considering the different delayed impulsive comparison systems. In addition, the Zeno behaviors could be successfully eliminated with the proposed event-triggered function. Finally, one numerical example is presented to validate the results.