2019年8月5日，数据恢复四川省重点实验室复杂网络与保密通信方向曾德强老师团队在国际期刊《IEEE Transactions on Neural Networks and Learning Systems》（IF：11.683，SCI一区）在线发表题为《Pinning Synchronization of Directed Coupled Reaction-Diffusion Neural Networks With Sampled-Data Communications》的研究论文，该研究解决了有向耦合反应-扩散神经网络的抽样通信牵制同步问题。
Abstract：This paper focuses on the design of a pinning sampled-data control mechanism for the exponential synchronization of directed coupled reaction-diffusion neural networks (CRDNNs) with sampled-data communications (SDCs). A new Lyapunov–Krasovskii functional (LKF) with some sampled-instant-dependent terms is presented, which can fully utilize the actual sampling information. Then, an inequality is fifirst proposed, which effectively relaxes the restrictions of the positive defifiniteness of the constructed LKF. Based on the LKF and the inequality, suffificient conditions are derived to exponentially synchronize the directed CRDNNs with SDCs. The desired pinning sampled-data control gain is precisely obtained by solving some linear matrix inequalities (LMIs). Moreover, a less conservative exponential synchronization criterion is also established for directed coupled neural networks with SDCs. Finally, simulation results are provided to verify the effectiveness and merits of the theoretical results.