School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, PR China.
School of Information Science and Engineering, Northeastern University, Shenyang 110819, PR China.
Neural Netw. 2020 May;125:31-40. doi: 10.1016/j.neunet.2020.01.024. Epub 2020 Feb 6.
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.
本文研究了离散时间神经网络的事件触发同步控制。主要亮点有三：（1）提出了一种新的事件触发机制（ETM），它可以看作是离散时间周期采样控制和连续 ETM 之间的切换；（2）设计了一个带有两个切换增益的饱和控制器，以匹配所提出的 ETM 的切换特性；（3）构建了一个专用的切换 Lyapunov-Krasovskii 泛函，它考虑了控制输入的锯齿约束。基于这些要素，推导出了同步准则，使得所考虑的误差系统局部稳定。随后，讨论了两个共同设计问题，分别是最大化允许的初始条件集和触发阈值。最后，通过两个数值示例验证了所提出方法的有效性和优势。
