Preisach function identification by neural networks

Author

Cirrincione, Maurizio ; Miceli, Rosario ; Galluzzo, Giuseppe Ricco ; Trapanese, Marco

Author_Institution

CNR, Palermo Univ., Italy

Volume

38

Issue

5

fYear

2002

fDate

9/1/2002 12:00:00 AM

Firstpage

2421

Lastpage

2423

Abstract

This paper presents a novel technique for the identification of the Preisach density function which is based on a neural-network approach and which requires a relatively limited amount of experimental parameters. The fundamental idea of this method is to identify Preisach function of the material by training a neural network with a set of loops whose identification function is known. In the final section of the paper, the method is verified on several cases.

Keywords

Gaussian distribution; coercive force; learning (artificial intelligence); magnetic hysteresis; multilayer perceptrons; parameter estimation; physics computing; remanence; soft magnetic materials; Gaussian function; Levenberg-Marquadt training algorithm; Preisach density function identification; coercive field; hysteresis loop; magnetization state; multilayer perceptron; neural network approach; remanence; soft magnetic materials; Density functional theory; Distribution functions; Helium; Magnetic hysteresis; Magnetic materials; Magnetic properties; Magnetization; Neural networks; Neurons; Soft magnetic materials;

fLanguage

English

Journal_Title

Magnetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9464

Type

jour

DOI

10.1109/TMAG.2002.803614

Filename

1042208