基于事件触发控制策略的具有时变延迟的忆阻型Cohen-Grossberg神经网络的渐近同步

Yao Wei, Yu Fei, Zhang Jin, Zhou Ling

School of Computer and Communication Engineering, Changsha University of Science & Technology, Changsha 410114, China.

Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310058, China.

Micromachines (Basel). 2022 Apr 30;13(5):726. doi: 10.3390/mi13050726.

This paper investigates the asymptotic synchronization of memristive Cohen-Grossberg neural networks (MCGNNs) with time-varying delays under event-triggered control (ETC). First, based on the designed feedback controller, some ETC conditions are provided. It is demonstrated that ETC can significantly reduce the update times of the controller and decrease the computing cost. Next, some sufficient conditions are derived to ensure the asymptotic synchronization of MCGNNs with time-varying delays under the ETC method. Finally, a numerical example is provided to verify the correctness and effectiveness of the obtained results.

本文研究了事件触发控制（ETC）下具有时变延迟的忆阻Cohen-Grossberg神经网络（MCGNNs）的渐近同步问题。首先，基于所设计的反馈控制器，给出了一些ETC条件。结果表明，ETC可以显著减少控制器的更新次数并降低计算成本。其次，推导了一些充分条件，以确保在ETC方法下具有时变延迟的MCGNNs的渐近同步。最后，给出了一个数值例子来验证所得结果的正确性和有效性。