Automatic and accurate evaluation of the parameters of a magnetic hysteresis model

Author

Grimaldi, Domenico ; Michaeli, Linus ; Palumbo, Arrigo

Author_Institution

Dipt. di Elettronica, Inf., e Sistemistica, Calabria Univ., Italy

Volume

49

Issue

1

fYear

2000

fDate

2/1/2000 12:00:00 AM

Firstpage

154

Lastpage

160

Abstract

This paper presents a method based on both artificial neural networks (ANNs) and on a multidimensional optimization procedure in order to significantly reduce the time taken and to improve the accuracy in evaluating parameters of the Jiles-Atherton model of magnetic hysteresis. The main steps of the method are (1) data acquisition of the experimental hysteresis loop of the magnetic material under test, (2) evaluation of the model´s parameters by means of ANN, and (3) parameter accuracy improvement by means of a multidimensional optimization procedure. In order to highlight the method´s effectiveness, the results of numerical and experimental tests are also given

Keywords

backpropagation; electrical engineering computing; feedforward neural nets; magnetic cores; magnetic domain walls; magnetic hysteresis; modelling; optimisation; parameter estimation; Jiles-Atherton model; PSPICE; artificial neural networks; automatic accurate evaluation; backpropagation; data acquisition; domain wall motion; domain wall pinning; feedforward network; hysteresis loop; learning phase; magnetic core; magnetic hysteresis model; magnetic induction; magnetic material under test; model parameters evaluation; multidimensional optimization; parameter accuracy improvement; Artificial neural networks; Magnetic devices; Magnetic domain walls; Magnetic hysteresis; Magnetic materials; Multidimensional systems; Optimization methods; Saturation magnetization; Shape measurement; Velocity measurement;

fLanguage

English

Journal_Title

Instrumentation and Measurement, IEEE Transactions on

Publisher

ieee

ISSN

0018-9456

Type

jour

DOI

10.1109/19.836327

Filename

836327