Global exponential stability of recurrent neural networks for solving optimization and related problems

Author

Xia, Youshen ; Wang, Jun

Author_Institution

Dept. of Autom. & Comput.-Aided Eng., Chinese Univ. of Hong Kong, Shatin, China

Volume

11

Issue

4

fYear

2000

fDate

7/1/2000 12:00:00 AM

Firstpage

1017

Lastpage

1022

Abstract

Global exponential stability is a desirable property for dynamic systems. The paper studies the global exponential stability of several existing recurrent neural networks for solving linear programming problems, convex programming problems with interval constraints, convex programming problems with nonlinear constraints, and monotone variational inequalities. In contrast to the existing results on global exponential stability, the present results do not require additional conditions on the weight matrices of recurrent neural networks and improve some existing conditions for global exponential stability. Therefore, the stability results in the paper further demonstrate the superior convergence properties of the existing neural networks for optimization

Keywords

asymptotic stability; convergence; convex programming; linear programming; recurrent neural nets; convergence properties; dynamic systems; global exponential stability; interval constraints; monotone variational inequalities; nonlinear constraints; Asymptotic stability; Convergence; Design optimization; Linear matrix inequalities; Linear programming; Neural networks; Recurrent neural networks; Roundoff errors; Stability analysis; Sufficient conditions;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.857782

Filename

857782