Title :
Global stability of a class of neural networks with time-varying delay
Author :
Ensari, Tolga ; Arik, Sabri
Author_Institution :
Dept. of Comput. Eng., Istanbul Univ., Turkey
fDate :
3/1/2005 12:00:00 AM
Abstract :
This paper presents a new sufficient condition for the uniqueness and global asymptotic stability of the equilibrium point for a class of neural networks with time-varying delays. The result is obtained by the use of a more general type of Lyapunov-Krasovskii functional, establishing a relation between the network parameters of the neural system and time-varying delay parameter. The result is also shown to be a generalization of a previously published result.
Keywords :
Lyapunov matrix equations; asymptotic stability; delays; neural nets; numerical stability; Lyapunov-Krasovskii functional; equilibrium analysis; global stability; neural networks; stability analysis; sufficient condition; time-varying delay; Associative memory; Asymptotic stability; Cellular neural networks; Design optimization; Differential equations; Hopfield neural networks; Neural networks; Neurons; Stability analysis; Sufficient conditions; Equilibrium and stability analysis; Lyapunov functionals; neural networks; time-varying delays;
Journal_Title :
Circuits and Systems II: Express Briefs, IEEE Transactions on
DOI :
10.1109/TCSII.2004.842050
