Exponential stability of the neural networks with time-varying discrete and distributed delays

Author

Li, Qingbo ; Wang, Shujuan ; Wu, Yuanyuan

Author_Institution

Dept. of Math. & Inf. Sci., Zhengzhou Univ. of Light Ind., Zhengzhou, China

fYear

2010

fDate

26-28 May 2010

Firstpage

2453

Lastpage

2458

Abstract

For a class of generalized neural networks(NNs) with discrete and distributed time-varying delays, this paper is concerned with the problems of determining the global exponential stability and estimating the exponential convergence rate. By introducing a novel Lyapunov-Krasovskii functional and some appropriate free-weighting matrices, a new delay-dependent stability criterion is derived in terms of linear matrix inequalities (LMIs). Finally, a numerical examples is given to show the superiority of the obtained results.

Keywords

Lyapunov methods; asymptotic stability; delay systems; discrete time systems; linear matrix inequalities; neurocontrollers; stability criteria; time-varying systems; Lyapunov-Krasovskii functional; delay-dependent stability criterion; discrete time-varying delay; distributed time-varying delays; exponential convergence rate; free-weighting matrix; generalized neural network; global exponential stability; linear matrix inequalities; Artificial neural networks; Convergence; Delay effects; Delay estimation; Linear matrix inequalities; Neural networks; Neurons; Stability analysis; Stability criteria; Symmetric matrices; Exponential stability; Neural networks; Time-varying delay; linear matrix inequalities;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2010 Chinese

Conference_Location

Xuzhou

Print_ISBN

978-1-4244-5181-4

Electronic_ISBN

978-1-4244-5182-1

Type

conf

DOI

10.1109/CCDC.2010.5498783

Filename

5498783