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Bipartite synchronization for inertia memristor-based neural networks on coopetition networks.
Neural Networks ( IF 7.8 ) Pub Date : 2019-11-29 , DOI: 10.1016/j.neunet.2019.11.010
Ning Li 1 , Wei Xing Zheng 2
Affiliation  

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.

中文翻译:

Coopetition网络上基于惯性忆阻器的神经网络的二部同步。

本文讨论了基于耦合惯性忆阻器的具有协作和竞争相互作用的神经网络的二部同步问题。通常,竞争竞争网络是通过有符号图建模的,并且相应的拉普拉斯矩阵与非负图不同。具有结构平衡的竞合网络可以达到幅度相同但符号相反的最终状态,这称为二分同步。另外,惯性系统是二阶微分系统。在本文中,首先,通过使用适当的变量替换,将基于惯性忆阻器的神经网络(IMNN)转换为一阶微分方程。其次,通过设计合适的不连续控制器,获得了在竞争网络上有或没有前导节点的IMNN的双向同步准则。最后,提供了两个带有仿真的说明性示例,以验证所提出的不连续控制策略对实现两方同步的有效性。
更新日期:2019-11-30
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