Exponential stability of impulsive high-order Hopfield-type neural networks with time-varying delays

Author

Liu, Xinzhi ; Teo, Kok Lay ; Xu, Bingji

Author_Institution

Dept. of Appl. Math., Univ. of Waterloo, Ont., Canada

Volume

16

Issue

6

fYear

2005

Firstpage

1329

Lastpage

1339

Abstract

This paper considers the problems of global exponential stability and exponential convergence rate for impulsive high-order Hopfield-type neural networks with time-varying delays. By using the method of Lyapunov functions, some sufficient conditions for ensuring global exponential stability of these networks are derived, and the estimated exponential convergence rate is also obtained. As an illustration, an numerical example is worked out using the results obtained.

Keywords

Hopfield neural nets; Lyapunov methods; asymptotic stability; convergence; delay estimation; neural nets; numerical stability; time-varying systems; Lyapunov function; exponential convergence rate; exponential stability; impulsive high-order Hopfield-type neural network; time-varying delay; Convergence; Delay effects; Hopfield neural networks; Lyapunov method; Mathematics; Neural networks; Neurons; Pattern recognition; Stability; Sufficient conditions; Exponential stability; impulsive high-order Hopfield-type Lyapunov function; neural networks; Algorithms; Computer Simulation; Models, Statistical; Neural Networks (Computer); Nonlinear Dynamics; Signal Processing, Computer-Assisted; Time Factors;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2005.857949

Filename

1528514