Stability of Hopfield neural networks with time delays and variable-time impulses

被引：11
|
作者：
Liu, Chao [1 ]
Li, Chuandong [1 ]
Huang, Tingwen [2 ]
Li, Chaojie [1 ]
机构：
[1] Chongqing Univ, Coll Comp, Chongqing 400044, Peoples R China
[2] Texas A&M Univ Qatar, Doha, Qatar
来源：
NEURAL COMPUTING & APPLICATIONS | 2013年 / 22卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Hopfield neural network (HNN); Time delays; Variable-time impulse; Stability; EXPONENTIAL STABILITY; DIFFERENTIAL-EQUATIONS; COMPARISON PRINCIPLE; GRADED RESPONSE; VARYING DELAYS; EXISTENCE; NEURONS;
D O I：
10.1007/s00521-011-0695-2
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The stability of Hopfield neural networks with fixed-time impulses has been intensively investigated in recent years. However, few existing publications addressed the stability of delayed neural networks with variable-time impulses. In this paper, we consider the case of variable-time impulses and attempt to establish the general stability criteria. It shows that the proposed results can also be applied to the case of fixed-time impulses, which provide a new stability condition for the case of fixed-time impulses. To illustrate the effectiveness of our theoretical results, numerical examples and simulations are also presented.
引用
收藏
页码：195 / 202
页数：8
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