Global robust exponential stability for Hopfield neural networks with non-Lipschitz activation functions

被引：0
|
作者：
Hongtao Yu
Huaiqin Wu
机构：
[1] Yanshan University,College of Information Science and Engineering
[2] Yanshan University,College of Science
来源：
Journal of Mathematical Sciences | 2012年 / 187卷 / 4期
关键词：
Linear Matrix Inequality; Robust Stability; Cellular Neural Network; Bidirectional Associative Memory Neural Network; Unique Equilibrium Point;
D O I：
10.1007/s10958-012-1079-6
中图分类号：
学科分类号：
摘要：
We consider the problem of global robust exponential stability for Hopfield neural networks with norm-bounded parameter uncertainties and inverse Hölder neuron activation functions. By using the Brouwer degree properties and some analysis techniques, we investigate the existence and uniqueness of an equilibrium point. Based on the Lyapunov stability theory, we derive a global robust exponential-stability criterion in terms of a linear matrix inequality (LMI). Two numerical examples are provided to demonstrate the efficiency and validity of the proposed robust stability results.
引用
收藏
页码：511 / 523
页数：12
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