Finite-time synchronization of memristor-based Cohen-Grossberg neural networks with time-varying delays

被引：42
|
作者：
Liu, Mei [1 ]
Jiang, Haijun [1 ]
Hu, Cheng [1 ]
机构：
[1] Xinjiang Univ, Coll Math & Syst Sci, Urumqi 830046, Xinjiang, Peoples R China
来源：
NEUROCOMPUTING | 2016年 / 194卷
关键词：
Cohen-Grossberg neural network; Time-varying delay; Finite-time synchronization; Memristor; PERIODICALLY INTERMITTENT CONTROL; EXPONENTIAL LAG SYNCHRONIZATION; ADAPTIVE SYNCHRONIZATION; DYNAMIC-BEHAVIORS; STABILITY; IMPULSES; SYSTEMS;
D O I：
10.1016/j.neucom.2016.02.012
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 concerns the problem of global and local finite-time synchronization for a class of memristor-based Cohen-Grossberg neural networks with time-varying delays by designing an appropriate feedback controller. Through a nonlinear transformation, we derive an alternative system from the considered memristor-based Cohen-Grossberg neural networks. Then, by considering the finite-time synchronization of the alternative system, we obtain some novel and effective finite-time synchronization criteria for the considered memristor-based Cohen-Grossberg neural networks. These results generalize and extend some previous known works on conventional Cohen-Grossberg neural networks. Finally, numerical simulations are given to present the effectiveness of the theoretical results. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码：1 / 9
页数：9
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