Nonfragile Finite-Time Synchronization for Coupled Neural Networks With Impulsive Approach
Hongxia Rao, Yuru Guo, Yong Xu, Chang Liu, Renquan Lu
Abstract
This article addresses the problem of the average stochastic finite-time synchronization (ASFTS) for a set of coupled neural networks (NNs) with energy-bounded noises. Due to the channel capacity constraint, the impulsive approach is introduced so as to cut down the communication times among the leader NNs and the follower NNs. Then, a nonfragile controller is designed to improve the robustness of the controller with randomly occurred uncertainty. The sufficient conditions that guarantee the ASFTS of the coupled NNs and the leader NNs are achieved. The boundary of the synchronization error is also obtained by constructing the monotonic increasing functions. Finally, the controller gains are given based on the derived conditions, and their effectiveness is illustrated by a numerical example.