Robust Stability of Switched Cohen–Grossberg Neural Networks With Mixed Time-Varying Delays

Author

Yuan, Kun ; Cao, Jinde ; Li, Han-Xiong

Author_Institution

Dept. of Math., Southeast Univ., Nanjing

Volume

36

Issue

6

fYear

2006

Firstpage

1356

Lastpage

1363

Abstract

By combining Cohen-Grossberg neural networks with an arbitrary switching rule, the mathematical model of a class of switched Cohen-Grossberg neural networks with mixed time-varying delays is established. Moreover, robust stability for such switched Cohen-Grossberg neural networks is analyzed based on a Lyapunov approach and linear matrix inequality (LMI) technique. Simple sufficient conditions are given to guarantee the switched Cohen-Grossberg neural networks to be globally asymptotically stable for all admissible parametric uncertainties. The proposed LMI-based results are computationally efficient as they can be solved numerically using standard commercial software. An example is given to illustrate the usefulness of the results

Keywords

Lyapunov methods; delays; linear matrix inequalities; neural nets; stability; time-varying systems; Lyapunov approach; linear matrix inequality technique; mixed time-varying delay; robust stability; standard commercial software; switched Cohen-Grossberg neural network; Delay effects; Diversity reception; Hopfield neural networks; Linear matrix inequalities; Mathematical model; Neural networks; Robust stability; Sufficient conditions; Switched systems; Time varying systems; Cohen–Grossberg neural networks; mixed time-varying delays; robust stability; switched systems; uncertain systems;

fLanguage

English

Journal_Title

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

1083-4419

Type

jour

DOI

10.1109/TSMCB.2006.876819

Filename

4014591