Exponential lag synchronization for delayed memristive recurrent neural networks

被引：27
|
作者：
Zhang, Guodong [1 ]
Hu, Junhao [1 ]
Shen, Yi [2 ]
机构：
[1] South Cent Univ Nationalities, Coll Math & Stat, Wuhan 430074, Peoples R China
[2] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Peoples R China
来源：
NEUROCOMPUTING | 2015年 / 154卷
基金：
美国国家科学基金会; 中国国家自然科学基金;
关键词：
Lag synchronization; Memristive recurrent neural networks; Nonsmooth analysis; Time delays; TIME-VARYING DELAYS; PERIODICALLY INTERMITTENT CONTROL; GLOBAL ASYMPTOTIC STABILITY; GENERAL-CLASS; MULTIPLE DELAYS; MIXED DELAYS; BEHAVIORS; IMPULSES; DISCRETE; DYNAMICS;
D O I：
10.1016/j.neucom.2014.12.016
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 paper, by using the parallel-memristors connection corresponding to the capacitors and memristors synaptic connection in usual recurrent neural networks, general delayed memristive recurrent neural networks are proposed. Basing on nonsmooth analysis and control theory, several sufficient conditions concerning global exponential lag synchronization are obtained for the proposed system. In addition, the obtained results complement and extend earlier publications on memristive or conventional neural network dynamical systems with continuous or discontinuous right-hand side. Finally, numerical simulations illustrate the effectiveness of our results. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码：86 / 93
页数：8
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