Dynamic Magnetic Hysteresis Modelling Using Elman Recurrent Neural Network
Date
2001
Authors
Saghafifar, M.
Nafalski, A.
Editors
Takagi, T.
Uesaka, M.
Uesaka, M.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
International Journal of Applied Electromagnetics and Mechanics, 2001 / Takagi, T., Uesaka, M. (ed./s), vol.13, iss.1-4 SPEC., pp.209-214
Statement of Responsibility
Conference Name
10th International Symposium on Applied Electromagnetics and s (13 May 2001 : Japan)
Abstract
<jats:p>Dynamic magnetic hysteresis modelling is of crucial importance in determining the electromagnetic behaviour of magnetic cores. Among the different models of magnetic hysteresis, Preisach model is one of the more commonly applied. The dynamic Preisach model is a generalisation of the static Preisach model, and is obtained by adding the rate of change of the output variable, which makes the numerical implementation more complex.</jats:p>
<jats:p>Since dynamic hysteresis models require mapping of time dependent sequences, a feature available in some neural networks, in this paper the application of recurrent neural networks to model dynamic hysteresis was investigated. An Elman neural network was selected to test the concept. The network was trained with a set of measured dynamic data and was tested with another set of experimental data, showing acceptable accuracy.</jats:p>