School of Mathematics and Statistics, Zhoukou Normal University, Zhoukou, 466001, Henan, China.
College of Mathematics and System Science, Xinjiang University, Urumqi, 830046, Xinjiang, China.
Neural Netw. 2023 Sep;166:524-540. doi: 10.1016/j.neunet.2023.07.034. Epub 2023 Aug 2.
This work aims to achieve cluster synchronization of a complex network by some pinning control strategies. Firstly, the network not only is affected by the reaction-diffusion and the directed coupling phenomena, but also is disturbed by the stochastic noise and Markovian switching. Secondly, switched constant gain pinning, centralized and decentralized adaptive pinning are proposed respectively to realize the cluster synchronization of the considered network. In these adaptive pinning controllers, the control gain and coupling strength can been adjusted automatically while only a part of the nodes are controlled. Thirdly, the target state of cluster synchronization is taken as the average state related to the directed topology of all nodes in the same cluster, and does not need to be given separately as an isolated node. Finally, to verify the theoretical results, some simulations of directed coupled reaction-diffusion neural networks with stochastic noise and Markovian switching are given.
本工作旨在通过一些钉扎控制策略实现复杂网络的簇同步。首先,网络不仅受到反应扩散和有向耦合现象的影响,而且还受到随机噪声和马尔可夫切换的干扰。其次,分别提出了切换常数增益钉扎、集中式和分散式自适应钉扎,以实现所考虑网络的簇同步。在这些自适应钉扎控制器中,控制增益和耦合强度可以在仅控制部分节点的情况下自动调整。第三,簇同步的目标状态被视为与同一簇中所有节点的有向拓扑相关的平均状态,而不需要作为单独的节点分别给出。最后,为了验证理论结果,给出了具有随机噪声和马尔可夫切换的有向耦合反应扩散神经网络的一些仿真。