A new LMI condition for delay-dependent asymptotic stability of delayed Hopfield neural networks

Author

Xu, Shengyuan ; Lam, James ; Ho, Daniel W C

Author_Institution

Dept. of Autom., Nanjing Univ. of Sci. & Technol., China

Volume

53

Issue

3

fYear

2006

fDate

3/1/2006 12:00:00 AM

Firstpage

230

Lastpage

234

Abstract

In this paper, a new delay-dependent asymptotic stability condition for delayed Hopfield neural networks is given in terms of a linear matrix inequality, which is less conservative than existing ones in the literature. This condition guarantees the existence of a unique equilibrium point and its global asymptotic stability of a given delayed Hopfield neural network. Examples are provided to show the reduced conservatism of the proposed condition.

Keywords

Hopfield neural nets; asymptotic stability; delays; linear matrix inequalities; LMI condition; delay-dependent asymptotic stability; delayed Hopfield neural networks; global asymptotic stability; linear matrix inequality; time delays; Artificial neural networks; Associative memory; Asymptotic stability; Delay effects; Educational programs; Hopfield neural networks; Image reconstruction; Linear matrix inequalities; Stability analysis; Symmetric matrices; Global asymptotic stability; Hopfield neural networks; linear matrix inequality; time delays;

fLanguage

English

Journal_Title

Circuits and Systems II: Express Briefs, IEEE Transactions on

Publisher

ieee

ISSN

1549-7747

Type

jour

DOI

10.1109/TCSII.2005.857764

Filename

1605440