Improved Delay-Dependent Stability Condition of Discrete Recurrent Neural Networks With Time-Varying Delays

Author

Wu, Zhengguang ; Su, Hongye ; Chu, Jian ; Zhou, Wuneng

Author_Institution

Nat. Lab. of Ind. Control Technol., Zhejiang Univ., Hangzhou, China

Volume

21

Issue

4

fYear

2010

fDate

4/1/2010 12:00:00 AM

Firstpage

692

Lastpage

697

Abstract

This brief investigates the problem of global exponential stability analysis for discrete recurrent neural networks with time-varying delays. In terms of linear matrix inequality (LMI) approach, a novel delay-dependent stability criterion is established for the considered recurrent neural networks via a new Lyapunov function. The obtained condition has less conservativeness and less number of variables than the existing ones. Numerical example is given to demonstrate the effectiveness of the proposed method.

Keywords

Lyapunov methods; asymptotic stability; delay systems; linear matrix inequalities; neurocontrollers; recurrent neural nets; time-varying systems; Lyapunov function; delay-dependent stability condition; discrete recurrent neural network; global exponential stability; linear matrix inequality; time-varying delay; Delay dependent; exponential stability; linear matrix inequality (LMI); neural networks; time-varying delays; Algorithms; Computer Simulation; Feedback; Humans; Linear Models; Neural Networks (Computer); Nonlinear Dynamics; Time Factors;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2010.2042172

Filename

5415544