Synchronization of Coupled Neural Networks With Constant Time-Delay Using Sampled-Data Information
研究了利用采样数据信息实现常时滞耦合神经网络同步的控制方法，提出了分布式控制协议，通过构造Lyapunov泛函和积分不等式给出充分条件，并优化最大采样间隔以降低通信能耗。
In this article, a synchronization control method is studied for coupled neural networks (CNNs) with constant time delay using sampled-data information. A distributed control protocol relying on the sampled-data information of neighboring nodes is proposed. Lyapunov functional is constructed to analyze the synchronization of CNNs with constant time delay. Using Park's integral inequality and improved free-weight matrix integral inequality, sufficient conditions are provided for CNNs to achieve synchronization with less conservatism. In addition, the maximum sampling interval is determined by transforming the sufficient conditions into an optimization problem, and an aperiodic sampling control technique is implemented to reduce the communication energy load. Finally, numerical simulations are provided to demonstrate that the proposed method is capable of achieving synchronization.