Exponential synchronization of a class of neural networks with sampled-data control

Abstract

This paper investigates the problem of the master-slave synchronization for a class of neural networks with discrete and distributed delays under sampled-data control. By introducing some new terms, a novel piecewise time-dependent Lyapunov-Krasovskii functional (LKF) is constructed to fully capture the available characteristics of real sampling information and nonlinear function vector of the system. Based on the LKF and Wirtinger-based inequality, less conservative synchronization criteria are obtained to guarantee the exponential stability of the error system, and then the slave system is synchronized with the master system. The designed sampled-data controller can be obtained by solving a set of linear matrix inequalities (LMIs), which depend on the maximum sampling period and the decay rate. The criteria are less conservative than the ones obtained in the existing works. A numerical example is presented to illustrate the effectiveness and merits of the proposed method.