Global asymptotic and exponential stability of a dynamic neural system with asymmetric connection weights

Author

Xia, Youshen ; Wang, Jun

Author_Institution

Nanjing Univ. of Posts & Telecommun., China

Volume

46

Issue

4

fYear

2001

fDate

4/1/2001 12:00:00 AM

Firstpage

635

Lastpage

638

Abstract

Recently, a dynamic neural system was presented and analyzed due to its good performance in optimization computation and low complexity for implementation. The global asymptotic stability of such a dynamic neural system with symmetric connection weights was well studied. In this note, based on a new Lyapunov function, we investigate the global asymptotic stability of the dynamic neural system with asymmetric connection weights. Since the dynamic neural system with asymmetric weights is more general than that with symmetric ones, the new results are significant in both theory and applications. Specially, the new result can cover the asymptotic stability results of linear systems as special cases

Keywords

Lyapunov methods; asymptotic stability; neural nets; system theory; Lyapunov function; asymmetric connection weights; dynamic neural system; exponential stability; global asymptotic stability; linear systems; low complexity; optimization computation; symmetric connection weights; Asymptotic stability; Automatic control; Control system synthesis; Control systems; Linear systems; Neurofeedback; Performance analysis; Stability analysis; Sufficient conditions; Time varying systems;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.917666

Filename

917666