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.

本文研究了两种不同的具有时滞和随机扰动的离散时间混沌神经网络的指数同步问题。此外，还考虑了主系统与其从系统之间的不可靠通信链路，将其建模为满足伯努利分布的随机数据丢失。利用Lyapunov泛函方法和随机分析理论，首先得到了误差动态系统均方指数稳定的充分条件。然后基于这样的充分条件，设计了一个可靠的控制器来保证两个不同的具有随机扰动的离散时间延迟神经网络在均方上呈指数同步。期望状态反馈控制器的参数可以通过求解线性矩阵不等式来实现。最后，给出了一个数值例子来验证所提出的同步方法的可行性和有效性。