New results for exponential stability of delayed cellular neural networks

Author

Senan, Sibel ; Arik, Sabri

Author_Institution

Dept. of Comput. Eng., Istanbul Univ., Turkey

Volume

52

Issue

3

fYear

2005

fDate

3/1/2005 12:00:00 AM

Firstpage

154

Lastpage

158

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;

fLanguage

English

Journal_Title

Circuits and Systems II: Express Briefs, IEEE Transactions on

Publisher

ieee

ISSN

1549-7747

Type

jour

DOI

10.1109/TCSII.2004.842045

Filename

1406207