This paper investigates the event-triggered synchronization control of discrete-time neural networks. The main highlights are threefold: (1) a new event-triggered mechanism (ETM) is presented, which can be regarded as a switching between the discrete-time periodic sampled-data control and a continuous ETM; (2) a saturating controller which is equipped with two switching gains is designed to match the switching property of the proposed ETM; (3) a dedicated switching Lyapunov-Krasovskii functional is constructed, which takes the sawtooth constraints of control input into account. Based on these ingredients, the synchronization criteria are derived such that the considered error systems are locally stable. Whereafter, two co-design problems are discussed to maximize the set of admissible initial conditions and the triggering threshold, respectively. Finally, the effectiveness and advantages of the proposed method are validated by two numerical examples.

中文翻译：

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离散时间神经网络的事件触发同步：一种切换方法。

本文研究了离散神经网络的事件触发同步控制。主要的亮点有三点：（1）提出了一种新的事件触发机制（ETM），可以将其视为离散时间周期采样数据控制与连续ETM之间的切换；（2）设计一个具有两个开关增益的饱和控制器，以匹配所提出的ETM的开关特性；（3）构建了专用的开关Lyapunov-Krasovskii功能，该功能考虑了控制输入的锯齿约束。基于这些成分，导出同步标准，以使所考虑的错误系统在本地稳定。此后，讨论了两个协同设计问题，以分别最大化可允许的初始条件和触发阈值的集合。