Boundary Sampled-Data Synchronization of Delayed Reaction-Diffusion Neural Networks
研究了带有时滞的反应扩散神经网络在边界采样数据控制下的同步问题，提出了基于边界和分布式采样测量的控制策略，并通过线性矩阵不等式求解控制增益，数值例子验证了方法的有效性。
We study the synchronization of delayed reaction-diffusion neural networks (RDNNs) with Neumann boundary conditions, considering both distributed and discrete delays. Particularly, boundary sampled-data (SD) control is proposed to synchronize delayed RDNNs. In the proposed synchronization strategy, boundary SD control is based on boundary and distributed SD measurements. Based on the Lyapunov stability theory and inequality techniques, some synchronization criteria via the boundary SD control are proposed for delayed RDNNs. The boundary SD control gains are obtained by solving the conditions with linear matrix inequalities. Finally, a numerical example is presented to demonstrate the feasibility and effectiveness of the proposed method.