Paper Info
Title
Bipartite synchronization for inertia memristor-based neural networks on coopetition networks
Abstract
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.
Year
DOI
Venue
2020
10.1016/j.neunet.2019.11.010
Neural Networks
Keywords
DocType
Volume
Memristive neural networks,Bipartite synchronization,Discontinuous control,Inertia term
Journal
124
Issue
ISSN
Citations 
1
0893-6080
1
PageRank 
References 
Authors
0.35
0
2
Name
Order
Citations
PageRank
Ning Li114548.40
Wei Xing Zheng24266274.73