详细信息
Bipartite synchronization for inertia memristor-based neural networks on coopetition networks ( SCI-EXPANDED收录 EI收录)
文献类型:期刊文献
英文题名:Bipartite synchronization for inertia memristor-based neural networks on coopetition networks
作者:Li, Ning[1,2];Zheng, Wei Xing[2]
第一作者:李宁;Li, Ning
通讯作者:Zheng, WX[1]
机构:[1]Henan Univ Econ & Law, Coll Math & Informat Sci, Zhengzhou 450046, Henan, Peoples R China;[2]Western Sydney Univ, Sch Comp Engn & Math, Sydney, NSW 2751, Australia
第一机构:河南财经政法大学数学与信息科学学院
通讯机构:[1]corresponding author), Western Sydney Univ, Sch Comp Engn & Math, Sydney, NSW 2751, Australia.
年份:2020
卷号:124
起止页码:39-49
外文期刊名:NEURAL NETWORKS
收录:;EI(收录号:20200408081064);Scopus(收录号:2-s2.0-85078196670);WOS:【SCI-EXPANDED(收录号:WOS:000518860600004)】;
基金:This work was supported in part by the National Natural Science Foundation of China under Grants 61603125 and 11872175, the Australian Research Council under Grant DP120104986, the Xinhe Huang Tingfang Young Scholars' Fund of HUEL, China under Grant hncjzfdxxhhtf201913, the Scientific and Technological Innovative Talents of Henan Province, China under Grant 20HASTIT024, the Chinese Scholarship Council under Grant 201708410029, the Key Program of Henan Universities, China under Grant 18A110003, and the NSW Cyber Security Network in Australia under Grant P00025091.
语种:英文
外文关键词:Memristive neural networks; Bipartite synchronization; Discontinuous control; Inertia term
摘要: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. (C) 2019 Elsevier Ltd. All rights reserved.
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