Dynamics of Competitive Neural Networks with Inverse Lipschitz Neuron Activations

被引：0
|
作者：
Nie, Xiaobing [1 ]
Cao, Jinde [1 ]
机构：
[1] Southeast Univ, Dept Math, Nanjing 210096, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2010, PT 1, PROCEEDINGS | 2010年 / 6063卷
关键词：
Competitive neural networks; Inverse Lipschitz neuron activations; Global exponential stability; GLOBAL EXPONENTIAL STABILITY; DIFFERENT TIME SCALES; DISTRIBUTED DELAYS;
D O I：
暂无
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, by using nonsmooth analysis approach, topological degree theory and Lyapunov-Krasovskii function method, the issue of global exponential stability is investigated for competitive neural networks possessing inverse Lipschitz neuron activations. Several novel sufficient conditions are established towards the existence, uniqueness and global exponential stability of the equilibrium point, for competitive neural networks with time-varying delay.
引用
收藏
页码：483 / 492
页数：10
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