Robust stability analysis of competitive neural networks with different time-scales under perturbations

被引：36
|
作者：
Meyer-Baese, A. [1 ]
Roberts, R. [1 ]
Yu, H. G. [1 ]
机构：
[1] Florida State Univ, Dept Elect & Comp Engn, Tallahassee, FL 32310 USA
来源：
NEUROCOMPUTING | 2007年 / 71卷 / 1-3期
关键词：
two-time-scale neural network; noise perturbation; robust stability; singularly perturbed system;
D O I：
10.1016/j.neucom.2007.08.014
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We establish robust stability results for competitive neural networks with different time-scales under parameter perturbations and determine conditions that ensure the existence of asymptotically stable equilibria of the perturbed neural system. It is assumed that the system uncertainties are limited by the upper bounds of their norms. We derive a Lyapunov function for the coupled system and a maximal upper bound for the fast time-scale associated with the neural activity state. (C) 2007 Elsevier B.V. All rights reserved.
引用
收藏
页码：417 / 420
页数：4
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