Novel Exponential Stability Criteria of High-Order Neural Networks With Time-Varying Delays

Author

Zheng, Cheng-De ; Zhang, Huaguang ; Wang, Zhanshan

Author_Institution

Dept. of Math., Dalian Jiaotong Univ., Dalian, China

Volume

41

Issue

2

fYear

2011

fDate

4/1/2011 12:00:00 AM

Firstpage

486

Lastpage

496

Abstract

The global exponential stability is analyzed for a class of high-order Hopfield-type neural networks with time-varying delays. Based on the Lyapunov stability theory, together with the linear matrix inequality approach and free-weighting matrix method, some less conservative delay-independent and delay-dependent sufficient conditions are presented for the global exponential stability of the equilibrium point of the considered neural networks. Two numerical examples are provided to demonstrate the effectiveness of the proposed stability criteria.

Keywords

Hopfield neural nets; Lyapunov methods; asymptotic stability; delays; linear matrix inequalities; time-varying systems; Lyapunov stability; exponential stability; free weighting matrix; high order Hopfield neural network; linear matrix inequality; time varying delays; Asymptotic stability; Delay effects; Educational institutions; Hopfield neural networks; Linear matrix inequalities; Lyapunov method; Neural networks; Robust stability; Stability analysis; Stability criteria; Free-weighting matrix method; global exponential stability; high-order neural networks; linear matrix inequality (LMI); Algorithms; Computer Simulation; Decision Support Techniques; Models, Theoretical; Neural Networks (Computer); Pattern Recognition, Automated;

fLanguage

English

Journal_Title

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

1083-4419

Type

jour

DOI

10.1109/TSMCB.2010.2059010

Filename

5549947