Exponential synchronization of two different discrete-time chaotic neural networks with time delays and stochastic missing data

Abstract

In this paper, the exponential synchronization problem of two different discrete-time chaotic neural networks with time delays and stochastic disturbances is investigated. In addition, the unreliable communication links are taken into account between the master system and its slave system, which are modelled as stochastic data dropouts satisfying Bernoulli distributions. By utilizing the Lyapunov functional approach and the stochastic analysis theory, a sufficient condition for the error dynamic system to be mean-square exponentially stable is first obtained. Then based on such sufficient condition, a reliable controller is designed to guarantee that two different discrete-time delayed neural networks with stochastic disturbances are exponentially synchronized in the mean square. The parameters of a desired state feedback controller can be achieved by solving in terms of linear matrix inequality. Finally, a numerical example is presented to validate the feasibility and effectiveness of the proposed synchronization approaches.