Design of delay-range-dependent state estimators for discrete-time recurrent neural networks with interval time-varying delay

Author

Lu, Chien-Yu ; Cheng, Jui-Chuan ; Su, Te-Jen

Author_Institution

Dept. of Ind. Educ. & Technol., Nat. Changhua Univ. of Educ., Changhua

fYear

2008

fDate

11-13 June 2008

Firstpage

4209

Lastpage

4213

Abstract

This paper performs a global stability analysis of a particular class of recurrent neural networks (RNN) with time-varying delay. Both Lipschitz continuous activation functions and monotone nondecreasing activation functions are considered. Globally delay-dependent robust stability criteria are derived in the form of linear matrix inequalities (LMI) through the use of Leibniz-Newton formula and relaxation matrices. Finally, two numerical examples are given to illustrate the effectiveness of the given criterion.

Keywords

delays; discrete time systems; linear matrix inequalities; neurocontrollers; robust control; state estimation; time-varying systems; Leibniz-Newton formula; Lipschitz continuous activation function; delay-dependent robust stability criteria; delay-range-dependent state estimator; discrete-time recurrent neural network; global stability analysis; linear matrix inequalities; relaxation matrices; time-varying delay; Delay effects; Delay estimation; Delay systems; Difference equations; Linear matrix inequalities; Neurons; Recurrent neural networks; Robust stability; Stability analysis; State estimation; Delay-range-dependent; interval time-varying delay; linear matrix inequality; state estimator;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 2008

Conference_Location

Seattle, WA

ISSN

0743-1619

Print_ISBN

978-1-4244-2078-0

Electronic_ISBN

0743-1619

Type

conf

DOI

10.1109/ACC.2008.4587154

Filename

4587154