New stability results of fractional-order Hopfield neural networks with delays

被引：0
|
作者：
Song Chao [1 ,2 ]
Cao Jinde [1 ,3 ]
Fei Shumin [1 ]
机构：
[1] Southeast Univ, Sch Automat, Nanjing 210096, Jiangsu, Peoples R China
[2] Nanjing Inst Technol, Dept Math & Phys, Nanjing 211167, Jiangsu, Peoples R China
[3] King Abdulaziz Univ, Dept Math, Fac Sci, Jeddah 21589, Saudi Arabia
来源：
PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016 | 2016年
基金：
中国国家自然科学基金;
关键词：
Fractional-order; Neural networks; Delay; Stability; FINITE-TIME STABILITY; SYNCHRONIZATION; DYNAMICS; CHAOS;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper aims to study delayed Hopfield neural networks with fractional-order Caputo derivative. With the help of the equivalent norm techniques and fixed point theorem, the new sufficient conditions to assure the existence, uniqueness and stability of fractional-order Hopfield neural networks with delays are obtained. Finally, the efficiency of the derived criteria is illustrated by two examples.
引用
收藏
页码：3561 / 3565
页数：5
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