College of Mathematics and Information Science, Henan University of Economics and Law, Zhengzhou, Henan, 450046, China; School of Computing, Engineering and Mathematics, Western Sydney University, Sydney NSW 2751, Australia.
School of Computing, Engineering and Mathematics, Western Sydney University, Sydney NSW 2751, Australia.
Neural Netw. 2020 Apr;124:39-49. doi: 10.1016/j.neunet.2019.11.010. Epub 2019 Nov 29.
This paper addresses the bipartite synchronization problem of coupled inertia memristor-based neural networks with both cooperative and competitive interactions. Generally, coopetition interaction networks are modeled by a signed graph, and the corresponding Laplacian matrix is different from the nonnegative graph. The coopetition networks with structural balance can reach a final state with identical magnitude but opposite sign, which is called bipartite synchronization. Additionally, an inertia system is a second-order differential system. In this paper, firstly, by using suitable variable substitutions, the inertia memristor-based neural networks (IMNNs) are transformed into the first-order differential equations. Secondly, by designing suitable discontinuous controllers, the bipartite synchronization criteria for IMNNs with or without a leader node on coopetition networks are obtained. Finally, two illustrative examples with simulations are provided to validate the effectiveness of the proposed discontinuous control strategies for achieving bipartite synchronization.
本文针对具有合作和竞争相互作用的耦合惯性忆阻神经网络的双边同步问题进行了研究。通常，合作竞争交互网络通过有向图进行建模，并且相应的拉普拉斯矩阵与非负图不同。具有结构平衡的合作竞争网络可以达到具有相同幅度但符号相反的最终状态，这被称为双边同步。此外，惯性系统是一个二阶微分系统。在本文中，首先，通过使用合适的变量替换，将基于惯性忆阻器的神经网络（IMNNs）转化为一阶微分方程。其次，通过设计合适的不连续控制器，得到了在竞争网络上具有或不具有领导者节点的 IMNNs 的双边同步准则。最后，通过两个具有仿真的实例验证了所提出的不连续控制策略对于实现双边同步的有效性。
