This paper studies the exponential synchronization of delayed neural networks (DNNs) on time scales using the intermittent event-triggered control (IETC) method. Initially, considering the time scale situation, an IETC that merges intermittent control and event-triggered control is introduced, and a new differential inequality is developed. Subsequently, an exponential synchronization criterion is established based on the Lyapunov function, the proposed differential inequality and the time scale theory, applicable to continuous, discrete and hybrid time domains. Furthermore, under the event-triggered condition, it is demonstrated that the lower bound of each event-triggered interval exceeds a positive constant, thereby preventing the occurrence of Zeno’s behavior. Finally, the approach efficacy is validated through numerical examples.

中文翻译：

---

时标上延迟神经网络指数同步的间歇事件触发控制


本文使用间歇事件触发控制（IETC）方法研究延迟神经网络（DNN）在时间尺度上的指数同步。首先，考虑到时间尺度的情况，引入了一种融合间歇控制和事件触发控制的IETC，并提出了一种新的微分不等式。随后，基于Lyapunov函数、提出的微分不等式和时间尺度理论，建立了适用于连续、离散和混合时域的指数同步准则。此外，在事件触发条件下，证明每个事件触发区间的下界都超过一个正常数，从而阻止了芝诺行为的发生。最后通过数值算例验证了该方法的有效性。