Asymptotic Synchronization Control of Discrete-Time Delayed Neural Networks With a Reuse Mechanism Under Missing Data and Uncertainty

This paper focuses on the mean-square asymptotic synchronization of discrete-time delayed neural networks with missing data and uncertainty. The unreliable communication links between neural networks are considered, and the process of missing data is modeled as a stochastic process that satisfies Bernoulli distribution. A delay-dependent criterion is given in the form of matrix inequalities using the Lyapunov function approach. Then, a feedback controller is designed based on a reuse mechanism, which avoids the fluctuation of the controller input compared with the existing literature to ensure that the master-slave system with uncertainties is asymptotically synchronized in mean square. Simulated annealing (SA) algorithm is used to obtain the controller. Finally, numerical examples are presented to illustrate the effectiveness of the theoretical result.