Exponential stability analysis of delayed memristor-based recurrent neural networks with impulse effects

被引：0
|
作者：
Huamin Wang
Shukai Duan
Chuandong Li
Lidan Wang
Tingwen Huang
机构：
[1] Southwest University,College of Electronic and Information Engineering
[2] Luoyang Normal University,Department of Mathematics
[3] Texas A&M University at Qatar,Department of Electrical and Computer Engineering
来源：
Neural Computing and Applications | 2017年 / 28卷
关键词：
Memristor-based recurrent neural networks; Exponential stability; Impulse effects; Impulsive differential inequality;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, a generalized memristor-based recurrent neural network model with variable delays and impulse effects is considered. By using an impulsive delayed differential inequality and Lyapunov function, the exponential stability of the impulsive delayed memristor-based recurrent neural networks is investigated. Several exponential and uniform stability criteria of this impulsive delayed system are derived, which promotes the study of memristor-based recurrent neural networks. Finally, the effectiveness of obtained results is illustrated by two numerical examples.
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页码：669 / 678
页数：9
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