Finite-time synchronization for delayed complex-valued neural networks via integrating inequality method

被引：67
|
作者：
Zhang, Zhengqiu [1 ]
Li, Ailing [1 ]
Yu, Shenghua [1 ]
机构：
[1] Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China
来源：
NEUROCOMPUTING | 2018年 / 318卷
关键词：
Finite-time synchronization; Complex-valued neural networks with time delays; Integral inequality method; Without applying some finite-time stability theorems; GLOBAL EXPONENTIAL STABILITY; VARYING DELAYS;
D O I：
10.1016/j.neucom.2018.08.063
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, we are concerned with the finite-time synchronization for a class of complex-valued neural networks with time delays. Instead of using some finite-time stability theorems which are recently widely applied to investigating the finite-time synchronization for neural networks, by means of using integral inequality method, two novel sufficient conditions on the finite-time synchronization for the above delayed complex-valued neural networks are established. Our results and method on finite-time synchronization for the above neural networks are new and complementary to the existing papers. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码：248 / 260
页数：13
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