The use of finite elements and neural networks for the solution of inverse electromagnetic problems

Author

Low, T.S. ; Chao, Bi

Author_Institution

Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore

Volume

28

Issue

5

fYear

1992

fDate

9/1/1992 12:00:00 AM

Firstpage

2811

Lastpage

2813

Abstract

A method that combines a neural network (NN) and the finite-element method is introduced for solving inverse electromagnetic field problems. This forms the basis for design synthesis. A two-layered NN with one-pass training is used in this scheme. It uses the information from the finite-element analysis for training and is very efficient and stable. The one-pass training of the NN leads to a time efficient scheme. The finite-element method is used to produce the training patterns and to analyze the optimized solution, and the neural network is used to optimize the parameters. With the use of the trained NN for optimization, the solution time for design optimization is reduced. An example of its use in the optimization of a permanent-magnet rotor configuration is presented

Keywords

electromagnetic fields; finite element analysis; learning (artificial intelligence); neural nets; power engineering computing; rotors; design synthesis; electromagnetic field; finite elements; inverse electromagnetic problems; neural networks; one-pass training; permanent-magnet rotor configuration; time efficient scheme; training patterns; Design optimization; Finite element methods; Mathematics; Neural networks; Neurons; Nonlinear equations; Pattern analysis; Polynomials; Stability; Supervised learning;

fLanguage

English

Journal_Title

Magnetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9464

Type

jour

DOI

10.1109/20.179635

Filename

179635