Title :
New results for exponential stability of delayed cellular neural networks
Author :
Senan, Sibel ; Arik, Sabri
Author_Institution :
Dept. of Comput. Eng., Istanbul Univ., Turkey
fDate :
3/1/2005 12:00:00 AM
Abstract :
This brief presents new sufficient conditions for the global exponential stability of the equilibrium point for delayed cellular neural networks (DCNNs). It is shown that the use of a more general type of Lyapunov-Krasovskii functional enables us to derive new results for exponential stability of the equilibrium point for DCNNs. The results establish a relation between the delay time and the parameters of the network. The results are also compared with one of the most recent results derived in the literature.
Keywords :
Lyapunov matrix equations; asymptotic stability; cellular neural nets; delays; function approximation; numerical stability; Lyapunov methods; Lyapunov-Krasovskii functional; delay time; delayed cellular neural networks; global exponential stability; sufficient conditions; Asymptotic stability; Cellular networks; Cellular neural networks; Delay effects; Eigenvalues and eigenfunctions; Lyapunov method; Neural networks; Stability criteria; Sufficient conditions; Symmetric matrices; Delays; Lyapunov methods; neural networks; stability;
Journal_Title :
Circuits and Systems II: Express Briefs, IEEE Transactions on
DOI :
10.1109/TCSII.2004.842045
