Intermittent Exponential Synchronization for Memristor-Based Neural Networks With Inertial Items and Mixed Time-Varying Delays

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.

Automated summary

This article investigates the problem of exponential synchronization for memristor-based neural networks with mixed time-varying delays and parameter perturbations. A periodically intermittent control protocol is designed to guarantee the exponential synchronization between two networks. The exponential synchronization criteria for these networks under the proposed controller are obtained using nonsmooth analysis, Halanay inequality, and Lyapunov theory. The synchronization of these networks is considered within the framework of a second-order system directly, which is different from existing literature.