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
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;
Conference_Titel :
Circuits and Systems, 2004. ISCAS '04. Proceedings of the 2004 International Symposium on
Print_ISBN :
0-7803-8251-X
DOI :
10.1109/ISCAS.2004.1329934
