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具有对抗性相互作用和切换拓扑结构的耦合反应扩散神经网络的牵制二分同步

Pinning bipartite synchronization for coupled reaction-diffusion neural networks with antagonistic interactions and switching topologies.

作者信息

Miao Baojun, Li Xuechen, Lou Jungang, Lu Jianquan

机构信息

School of Science, Xuchang University, Xuchang 461000, China.

Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, Huzhou University, Huzhou 313000, China.

出版信息

Neural Netw. 2021 Sep;141:174-183. doi: 10.1016/j.neunet.2021.04.007. Epub 2021 Apr 20.

Abstract

In this paper, the bipartite synchronization issue for a class of coupled reaction-diffusion networks with antagonistic interactions and switching topologies is investigated. First of all, by virtue of Lyapunov functional method and pinning control technique, we obtain some sufficient conditions which can guarantee that networks with signed graph topologies realize bipartite synchronization under any initial conditions and arbitrary switching signals. Secondly, for the general switching signal and periodic switching signal, a pinning controller that can ensure bipartite synchronization of reaction-diffusions networks is designed based on the obtained conditions. Meanwhile, a directed relationship between coupling strength and control gains is presented. Thirdly, numerical simulation is provided to demonstrate the correctness and validity of the derived theoretical results for reaction-diffusion systems. We briefly conclude our findings and future work.

摘要

本文研究了一类具有对抗性相互作用和切换拓扑结构的耦合反应扩散网络的二分同步问题。首先,借助李雅普诺夫泛函方法和牵制控制技术,我们得到了一些充分条件,这些条件能够保证具有符号图拓扑结构的网络在任意初始条件和任意切换信号下实现二分同步。其次,针对一般切换信号和周期切换信号,基于所得条件设计了一种能够确保反应扩散网络二分同步的牵制控制器。同时,给出了耦合强度与控制增益之间的一种定向关系。第三,提供了数值模拟以验证反应扩散系统所得理论结果的正确性和有效性。我们简要总结了研究结果和未来工作。

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