Global exponential stability in Lagrange sense for a class of neural networks with reaction-diffusion terms

Author

Luo Qi ; Zhang Yutian

Author_Institution

Coll. of Inf. & Control, Nanjing Univ. of Inf. Sci. & Technol., Nanjing, China

fYear

2011

fDate

22-24 July 2011

Firstpage

2787

Lastpage

2791

Abstract

With considering two types of bounded activation functions, the global exponential stability in Lagrange sense are considered for Cohen-Grossberg neural networks with time-varying delays and reaction-diffusion terms. By employing appropriate Lyapunov functions, Green formula and inequality techniques, several global exponential attractive sets are given in which all trajectories converge. The obtained results can also be applied to analyse monostable as well as multistable neural networks.

Keywords

asymptotic stability; delays; neural nets; reaction-diffusion systems; time-varying systems; transfer functions; Cohen-Grossberg neural networks; Green formula; Lagrange sense; appropriate Lyapunov functions; bounded activation functions; global exponential attractive sets; global exponential stability; inequality techniques; monostable neural networks; multistable neural networks; reaction-diffusion terms; time-varying delays; Artificial neural networks; Asymptotic stability; Circuit stability; Electronic mail; Stability criteria; Cohen-Grossberg Neural Network; Global Exponential Stability; Neutral Type; Time Delay;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2011 30th Chinese

Conference_Location

Yantai

ISSN

1934-1768

Print_ISBN

978-1-4577-0677-6

Electronic_ISBN

1934-1768

Type

conf

Filename

6001424