On global robust exponential stability of interval neural networks with delays

Author

Sun, Changyin ; Song, Shiji ; Feng, Chun-Bo

Author_Institution

Res. Inst. of Autom., Southeast Univ., Nanjing, China

Volume

3

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

2738

Lastpage

2742

Abstract

In this paper, based on globally Lipschitz continuous activation functions, new conditions ensuring existence, uniqueness and global robust exponential stability of the equilibrium point of interval neural networks with delays are obtained. The delayed Hopfield network, bidirectional associative memory network and cellular neural network are special cases of the network model considered. All the results obtained are generalizations of some recent results reported in the literature for neural networks with constant delays

Keywords

asymptotic stability; content-addressable storage; neural nets; transfer functions; Lipschitz continous activation functions; bidirectional associative memory network; cellular neural network; delayed Hopfield network; equilibrium point; exponential stability; interval neural networks; Associative memory; Convergence; Delay effects; Electronic mail; Fluctuations; Neural networks; Robust stability; Stability analysis; Sun; Very large scale integration;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1007580

Filename

1007580