Title :
Exponential stability for delayed cellular neural networks
Author :
Li, Xiao-Ping ; Jiao, Li-Cheng
Author_Institution :
Sch. of Mech.-Electron. Eng., Xidian Univ., Xi´´an, China
Abstract :
A new sufficient condition for global exponential stability and lower bounds on the rate of exponential convergence of delayed cellular neural networks (DCNNs) are obtained by means of a method based on delay differential inequality. The method, which does not make use of any Lyapunov functionals, is simple and effective for the stability analysis of neural networks with delay. Some previously established results in the literature are shown to be special cases of the presented result.
Keywords :
asymptotic stability; cellular neural nets; convergence; delay-differential systems; stability criteria; delay differential inequality; delayed cellular neural networks; exponential convergence rate; exponential stability sufficient condition; global exponential stability; stability analysis; Cellular neural networks; Convergence; Delay effects; Electronic mail; Image recognition; Neural networks; Pattern recognition; Radar signal processing; Stability analysis; Sufficient conditions;
Conference_Titel :
Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on
Print_ISBN :
0-7803-7508-4
DOI :
10.1109/ICMLC.2002.1176793
