Pinning Synchronization via Intermittent Control for Memristive Cohen-Grossberg Neural Networks With Mixed Delays

被引：9
|
作者：
Hui, Meng [1 ]
Luo, Ni [1 ]
Iu, Herbert Ho-Ching [2 ]
Wu, Qisheng [1 ]
Yao, Rui [1 ]
Bai, Lin [1 ]
机构：
[1] Changan Univ, Sch Elect & Control, Xian 710064, Peoples R China
[2] Univ Western Australia, Sch Elect Elect & Comp Engn, Perth, WA 6009, Australia
来源：
IEEE ACCESS | 2020年 / 8卷
基金：
中国国家自然科学基金;
关键词：
Delays; Synchronization; Biological neural networks; Memristors; Neurons; Stability analysis; Memristive Cohen-Grossberg neural networks; exponential synchronization; pinning control; periodic intermittent control; GLOBAL EXPONENTIAL STABILITY; FIXED-TIME SYNCHRONIZATION; DISCRETE; PASSIVITY; SYSTEMS;
D O I：
10.1109/ACCESS.2020.2982043
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents the exponential synchronization for a class of memristive Cohen-Grossberg neural networks (MCGNNs) with mixed delays via a new hybrid control strategy. This new hybrid control strategy combines pinning control and periodic intermittent control. According to the feature of memristor, the memristive terms of the MCGNNs with mixed delays are normalized by a simple linear transformation. Then the designed periodic intermittent control is added to selected partial network nodes. Based on the stability theory of memristive neural networks and the exponential synchronization rule, the new synchronization conditions are given. Finally, numerical simulations are provided to show the effectiveness of the theoretical method.
引用
收藏
页码：55676 / 55687
页数：12
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