Almost surely asymptotic stability of neutral stochastic neural networks with multiple time-varying delays

Author

Tan, Weiming ; Huang, Z.T. ; Qin, X.W.

Author_Institution

Sch. of Math. & Phys., Wuzhou Univ., Wuzhou, China

fYear

2011

fDate

15-17 July 2011

Firstpage

60

Lastpage

63

Abstract

In this paper, we study the almost surely asymptotic stability of neutral stochastic neural networks with multiple time-varying delays. By using Lyapunov-Krasovskii and linear matrix inequality approach, we obtain some sufficient conditions to ensure the stability of neutral stochastic neural networks. The results are show to be generalizations of some previously published results and are less conservative than existing results.

Keywords

Lyapunov methods; asymptotic stability; linear matrix inequalities; neurocontrollers; stochastic systems; Lyapunov-Krasovskii approach; asymptotic stability; linear matrix inequality; multiple time-varying delays; neutral stochastic neural network; Asymptotic stability; Biological neural networks; Circuit stability; Delay; Stability criteria; Almost surely asymptotic stability; Linear matrix inequality; Lyapunov functional; Multiple time-varying delays; Neutral stochastic neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechanic Automation and Control Engineering (MACE), 2011 Second International Conference on

Conference_Location

Hohhot

Print_ISBN

978-1-4244-9436-1

Type

conf

DOI

10.1109/MACE.2011.5986857

Filename

5986857