This study is related to the exponential synchronization problem of stochastic neural networks. A dynamic model of master-slave neural networks is established, which contains time-varying delays and Lévy noises. The main purpose is to enable the slave system to follow the master system under the condition of limited communication capacity. Both the master system and the slave system are affected by random noises. Some sufficient conditions are given by means of linear matrix inequality methods which are established by applying Lyapunov functional together with the generalized Dynkin’s formula. Furthermore, a discrete event-triggered control is adopted in master-slave systems, which not only reduces the transmission resources but also avoids the Zeno phenomenon. At last, a numerical example is provided to verify the usefulness of judgment conditions in this study.

这项研究与随机神经网络的指数同步问题有关。建立了主从神经网络的动态模型，该模型包含时变时滞和Lévy噪声。主要目的是使从属系统能够在通信容量有限的情况下跟随主系统。主系统和从系统都受到随机噪声的影响。利用线性矩阵不等式方法给出了一些充分的条件，这些方法是通过将Lyapunov泛函与广义Dynkin公式一起应用而建立的。此外，在主从系统中采用离散事件触发控制，不仅减少了传输资源，而且避免了芝诺现象。终于，