Finite-time synchronization for memristor-based BAM neural networks with stochastic perturbations and time-varying delays

摘要

This paper focuses on the finite-time synchronization issue of drive-response memristor-based bidirectional associative memory neural networks (MBAMNNs) with stochastic perturbations and time-varying delays. Based on the mathematical model of memristor, definition of finite-time stability for stochastic differential system and the drive-response concept, some novel sufficient conditions are given to ensure the finite-time synchronization of drive-response MBAMNNs with stochastic perturbations and time-varying delays. We design novel nonlinear feedback controllers to control the synchronization error to converge zero in a finite time, and the settling time for synchronization can be easily obtained. In addition, as two special cases, the finite-time synchronization control criteria for bidirectional associative memory neural networks with stochastic perturbations and time-varying delays and MBAMNNs without stochastic perturbations are also given. Finally, two numerical simulations are showed to demonstrate the correctness of main results.