Global robust criteria for stochastic neutral neural networks with uncertainties and unbounded distributed delay

Author

Liu, Guoquan ; Yang, Simon X.

Author_Institution

Coll. of Autom., Chongqing Univ., Chongqing, China

fYear

2011

fDate

20-23 March 2011

Firstpage

273

Lastpage

278

Abstract

The problem of global robust stability analysis is studied for a class of stochastic neutral neural networks with uncertainties and unbounded distributed delay. Novel stability criteria are obtained in terms of linear matrix inequality (LMI) by employing the Lyapunov-Krasovskii functional method and using the free-weighting matrices technique. In addition, two examples are given to show the effectiveness of the obtained conditions.

Keywords

Lyapunov methods; delays; linear matrix inequalities; neural nets; robust control; stability criteria; stochastic systems; Lyapunov-Krasovskii functional method; free-weighting matrices technique; global robust criteria; global robust stability analysis; linear matrix inequality; stability criteria; stochastic neutral neural networks; unbounded distributed delay; Artificial neural networks; Delay; Robustness; Stability criteria; Symmetric matrices; TV; global robust; lyapunov-krasovskii functional; stochastic neutral neral networks; unbounded distributed delays; uncertainties;

fLanguage

English

Publisher

ieee

Conference_Titel

Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2011 IEEE International Conference on

Conference_Location

Kunming

Print_ISBN

978-1-61284-910-2

Type

conf

DOI

10.1109/CYBER.2011.6011808

Filename

6011808