Dynamics in fractional-order neural networks

被引：111
|
作者：
Song, Chao [1 ,2 ]
Cao, Jinde [1 ,3 ]
机构：
[1] Southeast Univ, Dept Math, Nanjing 210096, Jiangsu, Peoples R China
[2] Nanjing Inst Technol, Dept Math & Phys, Nanjing 211167, Jiangsu, Peoples R China
[3] King Abdulaziz Univ, Fac Sci, Dept Math, Jeddah 21589, Saudi Arabia
来源：
NEUROCOMPUTING | 2014年 / 142卷
基金：
中国国家自然科学基金;
关键词：
Neural networks; Fractional order; Uniform stability; STABILITY; SYNCHRONIZATION; CHAOS;
D O I：
10.1016/j.neucom.2014.03.047
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 investigates a general class of neural networks with a fractional-order derivative. By using the contraction mapping principle, Krasnoselskii fixed point theorem and the inequality technique, some new sufficient conditions are established to ensure the existence and uniqueness of the nontrivial solution. Moreover, uniform stability of the fractional-order neural networks is proposed in fixed time-intervals. Finally, some examples are given to illustrate the effectiveness of theoretical results. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码：494 / 498
页数：5
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