A Neural-Network Based Model of the Magnetic Nonlinearity of a DC Electromagnet

Author

Díaz-Chacón, J.M. ; Ovando-Martínez, R. B B ; Hernández, C. ; Arjona, M.A.

Author_Institution

Div. de Estudios de Posgrado e Investig., Inst. Tecnol. de la Laguna, Torreón, Mexico

fYear

2010

fDate

Sept. 28 2010-Oct. 1 2010

Firstpage

205

Lastpage

210

Abstract

This paper shows how an Artificial Neural Network model (ANN) can be used to fit the nonlinear magnetic behavior of a DC electromagnet. An ANN model is trained to obtain a generalized function of the B²-v_r curve, which is commonly used in an electromagnetic model. Once the generalized function and its derivative are obtained, they are used to solve a magnetostatic nonlinear problem of a DC device using the finite element method and the Newton-Raphson algorithm.

Keywords

Newton-Raphson method; electromagnets; electronic engineering computing; finite element analysis; magnetic materials; magnetostatics; neural nets; ANN model; DC device; DC electromagnet; Newton-Raphson algorithm; artificial neural network model; electromagnetic model; finite element method; generalized function; magnetostatic nonlinear problem; nonlinear magnetic behavior; Artificial neural networks; Finite element methods; Magnetic flux; Magnetostatics; Neurons; Numerical models; Training; Artificial neural networks; electromagnetic model; finite element method; magnetization curve;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Robotics and Automotive Mechanics Conference (CERMA), 2010

Conference_Location

Morelos

Print_ISBN

978-1-4244-8149-1

Type

conf

DOI

10.1109/CERMA.2010.142

Filename

5692337