Abstract This study addresses the stability and stabilizability problems for complex-valued memristive neural networks (CVMNNs) with actuator failures via reliable aperiodic event-triggered sampled-data control. Different from the traditional control methods with time-triggered mechanism, an aperiodic event-triggered sampled-data control scheme is first proposed for CVMNNs. Taking the influence of actuator failures into account, a reliable controller is designed. In comparison with the existing control approaches, the one here is not only more applicable but effective to save the communication resources for CVMNNs. Then, a new Lyapunov–Krasovskii functional (LKF) is introduced, which can fully capture the information of sampling and complex-valued activation functions. Based on the LKF and some new estimation techniques, novel stability and stabilizability criteria are established, and the desired reliable aperiodic event-triggered sampled-data controller gains are obtained simultaneously. Finally, numerical simulations are provided to verify the effectiveness of the obtained theoretical results.

中文翻译：

---

具有执行器故障和非周期性事件触发采样数据控制的复值忆阻神经网络的可靠稳定性和稳定性

摘要 本研究通过可靠的非周期性事件触发采样数据控制解决了具有执行器故障的复值忆阻神经网络 (CVMNN) 的稳定性和稳定性问题。与具有时间触发机制的传统控制方法不同，首次提出了针对 CVMNN 的非周期性事件触发采样数据控制方案。考虑到执行器故障的影响，设计了一个可靠的控制器。与现有的控制方法相比，这里的方法不仅更适用，而且有效地节省了 CVMNN 的通信资源。然后，引入了一种新的 Lyapunov-Krasovskii 泛函（LKF），它可以完全捕获采样和复值激活函数的信息。基于 LKF 和一些新的估计技术，建立了新的稳定性和稳定性标准，同时获得了所需的可靠的非周期性事件触发采样数据控制器增益。最后，通过数值模拟验证了所得理论结果的有效性。