Bipartite Synchronization of Multiple Memristor-Based Neural Networks With Antagonistic Interactions

被引：50
作者：
Li, Ning [1 ,2 ]
Zheng, Wei Xing [2 ]
机构：
[1] Henan Univ Econ & Law, Coll Math & Informat Sci, Zhengzhou 450046, Peoples R China
[2] Western Sydney Univ, Sch Comp Data & Math Sci, Sydney, NSW 2751, Australia
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2021年 / 32卷 / 04期
基金：
澳大利亚研究理事会; 中国国家自然科学基金;
关键词：
Synchronization; Couplings; Neural networks; Adaptation models; Adaptive control; Mathematical model; bipartite synchronization; memristive neural networks; signed graph; ANTI-SYNCHRONIZATION; SYSTEMS;
D O I：
10.1109/TNNLS.2020.2985860
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this article, by introducing a signed graph to describe the coopetition interactions among network nodes, the mathematical model of multiple memristor-based neural networks (MMNNs) with antagonistic interactions is established. Since the cooperative and competitive interactions coexist, the states of MMNNs cannot reach complete synchronization. Instead, they will reach the bipartite synchronization: all nodes' states will reach an identical absolute value but opposite sign. To reach bipartite synchronization, two kinds of the novel node- and edge-based adaptive strategies are proposed, respectively. First, based on the global information of the network nodes, a node-based adaptive control strategy is constructed to solve the bipartite synchronization problem of MMNNs. Secondly, a local edge-based adaptive algorithm is proposed, where the weight values of edges between two nodes will change according to the designed adaptive law. Finally, two simulation examples validate the effectiveness of the proposed adaptive controllers and bipartite synchronization criteria.
引用
收藏
页码：1642 / 1653
页数：12
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