An analysis of global robust stability of delayed dynamical neural networks

被引：7
|
作者：
Yucel, Eylem [1 ]
机构：
[1] Istanbul Univ, Dept Comp Engn, TR-34320 Istanbul, Turkey
来源：
NEUROCOMPUTING | 2015年 / 165卷
关键词：
Neural networks; Delayed systems; Lyapunov Functionals; Stability analysis; EXPONENTIAL STABILITY; NORM;
D O I：
10.1016/j.neucom.2015.03.070
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper studies the problem of establishing robust asymptotic stability of neural networks with multiple time delays and in the presence of the parameter uncertainties of the network. A new sufficient condition ensuring robust asymptotic stability is presented by manipulating the properties of some certain classes of real matrices and employing Homomorphic mapping and Lyapunov stability theorems. A numerical example is given to show that the condition obtained can outperform alternative ones in terms of conservatism and computational complexity. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码：436 / 443
页数：8
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