Global asymptotic stability of a class of feedback neural networks with an application to optimization problems

被引：0
|
作者：
Zhaoshu Feng
Anthony N. Michel
机构：
[1] University of Notre Dame,Department of Electrical Engineering
来源：
Circuits, Systems and Signal Processing | 1998年 / 17卷
关键词：
Neural Network; Asymptotic Stability; Stability Testing; Stability Theory; Unique Equilibrium;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
By applying results from homotopy theory, new conditions are obtained for the existence and uniqueness of an equilibrium for a class of continuous-time feedback neural networks which contains the Hopfield model as a special case. Next, new criteria are established for the global asymptotic stability of the unique equilibrium of this class of neural networks by utilizing Lur'e-type Lyapunov functions and the stability theory for systems of differential inequalities. Several practical stability testing conditions are given. As a special case, criteria are derived for the global asymptotic stability of Hopfield neural networks. This is followed by a robustness analysis of the class of neural networks considered. The results obtained are then applied to an optimization problem.
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页码：219 / 241
页数：22
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