Global asymptotic stability analysis for stochastic neutral-type delayed neural networks

Author

Wei Feng ; Haixia Wu

Author_Institution

Dept. of Comput. Sci., Chongqing Educ. Coll., Chongqing, China

fYear

2010

fDate

26-28 May 2010

Firstpage

2658

Lastpage

2662

Abstract

In this paper, the global asymptotic stability is investigated for a class of neutral stochastic neural networks with time-varying delays. Based on Lyapunov stability theory and stochastic analysis approaches, delay-dependent criterion is derived to ensure the global, asymptotic stability of the addressed system in the mean square. The criterion can be checked easily by the LMI Control Toolbox in Matlab. A numerical example is given to illustrate the feasibility and effectiveness of the results.

Keywords

Lyapunov methods; asymptotic stability; delays; linear matrix inequalities; neural nets; stochastic processes; time-varying systems; LMI Control Toolbox; Lyapunov stability theory; delay-dependent criterion; delayed neural networks; global asymptotic stability; linear matrix inequalities; mean square method; stochastic analysis; stochastic neutral-type neural networks; time-varying delays; Asymptotic stability; Biological neural networks; Delay; Lyapunov method; Manipulator dynamics; Mathematical model; Neural networks; Neurotransmitters; Stability analysis; Stochastic processes; LMI; Stability; Stochastic Neutral-Type Delayed Neural Networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2010 Chinese

Conference_Location

Xuzhou

Print_ISBN

978-1-4244-5181-4

Electronic_ISBN

978-1-4244-5182-1

Type

conf

DOI

10.1109/CCDC.2010.5498735

Filename

5498735