DocumentCode
3380455
Title
Global asymptotic stability of a class of neural networks with time varying delays
Author
Ensari, Tolga ; Arik, Sabri ; Tavsanoglu, Vedat
Author_Institution
Dept. of Comput. Eng., Istanbul Univ., Turkey
Volume
5
fYear
2004
fDate
23-26 May 2004
Abstract
This work presents a new sufficient condition for the uniqueness and global asymptotic stability (GAS) of the equilibrium point for a larger class of neural networks with time varying delays. It is shown that the use of a more general type of Lyapunov-Krasovskii functional leads to establish global asymptotic stability of a larger class of delayed neural networks that the neural network model considered in some previous papers.
Keywords
Lyapunov methods; asymptotic stability; delay systems; eigenvalues and eigenfunctions; neural net architecture; time-varying systems; Lyapunov-Krasovskii functional; delayed neural networks; equilibrium point; global asymptotic stability; neural network model; sufficient condition; time varying delays; Asymptotic stability; Cellular neural networks; Computer networks; Delay effects; Equations; Hopfield neural networks; Linear matrix inequalities; Neural networks; Neurons; Sufficient conditions;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 2004. ISCAS '04. Proceedings of the 2004 International Symposium on
Print_ISBN
0-7803-8251-X
Type
conf
DOI
10.1109/ISCAS.2004.1329934
Filename
1329934
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