Robust exponential stability for interval neural networks with delays and non-Lipschitz activation functions

被引：40
|
作者：
Wu, Huaiqin [1 ]
Tao, Feng [1 ]
Qin, Leijie [1 ]
Shi, Rui [1 ]
He, Lijun [1 ]
机构：
[1] Yanshan Univ, Dept Appl Math, Qinhuangdao 066001, Peoples R China
来源：
NONLINEAR DYNAMICS | 2011年 / 66卷 / 04期
关键词：
Neural networks; Inverse Holder activation functions; Global robust stability; Brouwer degree; Linear matrix inequality; TIME-VARYING DELAYS; LMI APPROACH; CRITERIA; UNCERTAINTIES; SYSTEMS;
D O I：
10.1007/s11071-010-9926-9
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
In this paper, the global robust exponential stability of interval neural networks with delays and inverse Holder neuron activation functions is considered. By using linear matrix inequality (LMI) techniques and Brouwer degree properties, the existence and uniqueness of the equilibrium point are proved. By applying Lyapunov functional approach, a sufficient condition which ensures that the network is globally robustly exponentially stable is established. A numerical example is provided to demonstrate the validity of the theoretical results.
引用
收藏
页码：479 / 487
页数：9
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