Analysis and synthesis of a class of neural networks: variable structure systems with infinite grain

Author

Li, Jian-Hua ; Michel, Anthony N. ; Porod, Wolfgang

Author_Institution

Dept. of Electr. & Comput. Eng., Notre Dame Univ., IN, USA

Volume

36

Issue

5

fYear

1989

fDate

5/1/1989 12:00:00 AM

Firstpage

713

Lastpage

731

Abstract

An investigation was conducted of the qualitative properties of a class of neural networks described by a system of first-order ordinary differential equations with discontinuous right hand side. An efficient synthesis procedure is developed for this class of neural networks. The class of systems considered may be used as a representation of the analog Hopfield model with the nonlinearities having infinite gain. Also, under appropriate assumptions, the output of the class of systems considered may be viewed as representing the behavior of the discrete Hopfield model. Thus the results give insight into the qualitative behavior of the analog as well as the discrete Hopfield models, and they provide a means of designing such models. The applicability of the present results is demonstrated by several specific examples

Keywords

network analysis; network synthesis; neural nets; analog Hopfield model; analysis; discrete Hopfield model; examples; neural networks; nonlinearities; qualitative properties; synthesis procedure; system of first-order ordinary differential equations; variable structure systems with infinite grain; Circuits and systems; Differential equations; Hopfield neural networks; Network synthesis; Neural networks; Variable structure systems;

fLanguage

English

Journal_Title

Circuits and Systems, IEEE Transactions on

Publisher

ieee

ISSN

0098-4094

Type

jour

DOI

10.1109/31.31320

Filename

31320