This paper studies the stabilization problem of delayed memristive neural networks under event-triggered control. A refined switching event-trigger scheme that switches between variable sampling and continuous event-trigger can be designed by introducing an exponential decay term into the threshold function. Compared with the existing mechanisms, the proposed scheme can enlarge the interval between two successively triggered events and therefore can reduce the amount of triggering times. By constructing a time-dependent and piecewise-defined Lyapunov functional, a less-conservative criterion can be derived to ensure global stability of the closed-loop system. Based on matrix decomposition, equivalent conditions in linear matrix inequalities form of the above stability criterion can be established for the co-design of both the trigger matrix and the feedback gain. A numerical example is provided to demonstrate the effectiveness of the theoretical analysis and the advantages of the refined switching event-trigger scheme.

本文研究了事件触发控制下的延迟忆阻神经网络的稳定性问题。通过在阈值函数中引入指数衰减项，可以设计出一种在可变采样和连续事件触发之间切换的改进的切换事件触发方案。与现有机制相比，该方案可以增大两个连续触发事件之间的间隔，从而可以减少触发时间。通过构造时间相关且分段定义的Lyapunov函数，可以导出保守度较低的标准，以确保闭环系统的全局稳定性。基于矩阵分解，可以为触发矩阵和反馈增益的共同设计建立上述稳定性标准的线性矩阵不等式形式的等效条件。数值例子说明了理论分析的有效性以及改进的开关事件触发方案的优点。