Synchronization criteria for inertial memristor-based neural networks with linear coupling.

Abstract

This paper is concerned with the synchronization problem for an array of memristive neural networks with inertial term, linear coupling and time-varying delay. Since parameters in the connection weight matrices are state-dependent, that is to say, the connection weight matrices jump in certain intervals, the mathematical model of the coupled inertial memristive neural networks can be considered as an interval parametric uncertain system. Based on the interval parametric uncertainty theory, two different synchronization criteria for memristor-based neural networks are obtained by applying the p-matrix measure (p=1,2,∞,ω), Halanay inequality and constructing suitable Lyapunov-Krasovskii functionals. Two simulation examples with fully-connected and nearest neighboring topology are presented to demonstrate the efficiency of the obtained theoretical results.