Fuzzy logic for scaling finite element solutions of electromagnetic fields

Author

Satsios, Kostas J. ; Labridis, Dimitris P. ; Dokopoulos, Petros S.

Author_Institution

Dept. of Electr. & Comput. Eng., Aristotle Univ. of Thessaloniki, Greece

Volume

33

Issue

3

fYear

1997

fDate

5/1/1997 12:00:00 AM

Firstpage

2299

Lastpage

2308

Abstract

Artificial intelligence (AI) has been used to determine the quasi-stationary two-dimensional electromagnetic fields within rectangular boundaries. Amplitude and phase of magnetic vector potential have been calculated in an iron slot with an embedded current carrying conductor. A suitable fuzzy neural network (FNN) for scaling finite elements electromagnetic field calculations has been developed. FNN has been trained, using finite elements calculations within rectangular boundaries. Then, FNN has been used to calculate the field in a new geometry differing significantly from the geometries used for training. It was concluded that FNN may be used to scale results from one geometry to another with negligible errors

Keywords

electromagnetic field theory; finite element analysis; fuzzy neural nets; artificial intelligence; embedded current carrying conductor; finite element solution scaling; fuzzy logic; fuzzy neural network; iron slot; magnetic vector potential; quasi-stationary two-dimensional electromagnetic field; rectangular boundary; training; Artificial intelligence; Conductors; Current density; Electromagnetic fields; Finite element methods; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Geometry; Power system stability;

fLanguage

English

Journal_Title

Magnetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9464

Type

jour

DOI

10.1109/20.573846

Filename

573846