A New Estimation Method for Time–Space Sampled-Data Synchronization of RDNNs With Random Delays
研究了具有随机延迟的反应扩散神经网络的均方渐近同步问题，设计了一种时空采样数据控制器以节省网络通信资源，并提出了新的处理方法和扩展的庞加莱-维尔丁格不等式，得到了更简洁且保守性更小的同步判据。
The asymptotical synchronization in mean square of reaction–diffusion neural networks (RDNNs) with random delays is studied in this article. By sampling on both the time domain and spatial domain, a time–space sampled-data controller (TSSDC) is designed, which can efficiently save the network communication resources for RDNNs. A new processing method for the TSSDC is provided. Compared with the existing methods, the processing method here can capture more sampling information and is more concise. An extended Poincaré–Wirtinger inequality is proposed, which is in matrix form and less conservative. Then by constructing a sampling-dependent LKF, using the extended Poincaré–Wirtinger inequality and Hölder inequality, new mean square asymptotical synchronization criteria are set up for RDNNs with random delays, and the desired TSSDC gain is obtained. At length, a numerical example is given to verify the effectiveness and superiority of the obtained results.