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Bipartite synchronization for inertia memristor-based neural networks on coopetition networks
Neural Networks ( IF 5.785 ) Pub Date : 2019-11-29 , DOI: 10.1016/j.neunet.2019.11.010
Ning Li, Wei Xing Zheng

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.
更新日期:2019-11-30

 

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