Robust asymptotical stability for uncertain stochastic neural networks with discrete and distributed delays

Author

Wang, Shu-yun ; Wang, Shao-ying ; Li, Guo-gang ; Gao, Zhi-feng

Author_Institution

Dept. of Math., Coll. of Handan, Handan

Volume

2

fYear

2008

fDate

12-15 July 2008

Firstpage

815

Lastpage

819

Abstract

This paper investigates the problem of robust asymptotical stability for uncertain stochastic neural networks with discrete and distributed delays. Based on Lyapunov-Krasovskii functional and stochastic analysis method, new stability criteria is presented in terms of linear matrix inequalities to guarantee stochastic neural networks to be robustly asymptotically stable for all admissible parameter uncertainties, The criteria can be checked by utilizing the Matlab LMI toolbox. Two numerical examples are provided to demonstrate the feasibility of the proposed robust asymptotical stability criteria.

Keywords

Lyapunov methods; asymptotic stability; delays; linear matrix inequalities; neural nets; stochastic systems; uncertain systems; Lyapunov-Krasovskii functional; Matlab LMI toolbox; admissible parameter uncertainties; discrete delays; distributed delays; linear matrix inequalities; robust asymptotic stability; stability criteria; stochastic analysis method; uncertain stochastic neural networks; Asymptotic stability; Delay effects; Educational institutions; Linear matrix inequalities; Machine learning; Neural networks; Robust stability; Stability criteria; Stochastic processes; Uncertain systems; Linear matrix inequalities; Norm-bounded uncertainties; Robust asymptotical stability; Stochastic neural networks; Time delays;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2008 International Conference on

Conference_Location

Kunming

Print_ISBN

978-1-4244-2095-7

Electronic_ISBN

978-1-4244-2096-4

Type

conf

DOI

10.1109/ICMLC.2008.4620516

Filename

4620516