Intermittent Exponential Synchronization for Memristor-Based Neural Networks With Inertial Items and Mixed Time-Varying Delays
研究了含惯性项和混合时变时滞的忆阻神经网络在参数扰动下的指数同步问题，设计了周期间歇控制器，并直接基于二阶系统框架推导了同步判据，对控制理论研究者有参考价值。
This article investigates the exponential synchronization problem for a class of memristor-based neural networks with mixed time-varying delays and parameter perturbations, and inertial items are considered (MINNs). A periodically intermittent control protocol is designed to guarantee the exponential synchronization between two MINNs. Then, by adopting nonsmooth analysis, Halanay inequality, and Lyapunov theory, the exponential synchronization criteria for MINNs under the proposed controller are obtained. Furthermore, instead of the reduced-order method, the synchronization of MINNs is considered under the framework of the second-order system directly, which is different from existing literature. Some numerical simulations are presented to show the validity of the proposed criteria in the end.