Fixed-time synchronization of delayed Cohen-Grossberg neural networks based on a novel sliding mode

被引：48
作者：
Xiao, Jian [1 ]
Zeng, Zhigang [2 ]
Wu, Ailong [3 ]
Wen, Shiping [4 ]
机构：
[1] Jiangsu Univ, Sch Elect & Informat Engn, Zhenjiang 212013, Jiangsu, Peoples R China
[2] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China
[3] Hubei Normal Univ, Coll Math & Stat, Huangshi 435002, Hubei, Peoples R China
[4] Univ Technol Sydney, Fac Engn Informat Technol, Ctr Artificial Intelligence, Ultimo, NSW 2007, Australia
来源：
NEURAL NETWORKS | 2020年 / 128卷
关键词：
Cohen-Grossberg neural networks; Fixed-time synchronization; Sliding-mode surface; Discontinuous activations; FINITE-TIME; PROJECTIVE SYNCHRONIZATION; STABILIZATION; STABILITY; SYSTEMS; DESIGN;
D O I：
10.1016/j.neunet.2020.04.020
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper has discussed fixed-time synchronization of discontinuous Cohen-Grossberg neural networks with time-varying delays and matched disturbances based on sliding mode control technology. First, a novel sliding-mode surface is established. And, the dynamics on the sliding-mode surface can be achieved in the fixed time by employing the Gudermannian function. Then, considering the effect of delay, two different control schemes are introduced to ensure the fixed time reachability of the sliding mode. In addition, some useful criteria are given out for fixed-time synchronization of neural networks, and the setting time is formulated in a straightforward way. Finally, some examples and simulations are presented to verify the validity of the proposed results. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页码：1 / 12
页数：12
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