J-A Hysteresis Model Parameters Estimation using GA

This paper presents the Jiles and Atherton (J-A) hysteresis model parameter estimation for soft magnetic composite (SMC) material. The calculation of Jiles and Atherton hysteresis model parameters is based on experimental data and genetic algorithms (GA). Genetic algorithms operate in a given area o...

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Main Authors: Bogomir Zidaric, Damijan Miljavec
Format: Article
Language:English
Published: VSB-Technical University of Ostrava 2005-01-01
Series:Advances in Electrical and Electronic Engineering
Subjects:
Online Access:http://advances.utc.sk/index.php/AEEE/article/view/261
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author Bogomir Zidaric
Damijan Miljavec
author_facet Bogomir Zidaric
Damijan Miljavec
author_sort Bogomir Zidaric
collection DOAJ
description This paper presents the Jiles and Atherton (J-A) hysteresis model parameter estimation for soft magnetic composite (SMC) material. The calculation of Jiles and Atherton hysteresis model parameters is based on experimental data and genetic algorithms (GA). Genetic algorithms operate in a given area of possible solutions. Finding the best solution of a problem in wide area of possible solutions is uncertain. A new approach in use of genetic algorithms is proposed to overcome this uncertainty. The basis of this approach is in genetic algorithm built in another genetic algorithm.
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spelling doaj.art-0a34ef8107bb419bbabd7e27772c78a32023-05-14T20:50:05ZengVSB-Technical University of OstravaAdvances in Electrical and Electronic Engineering1336-13761804-31192005-01-0143174177285J-A Hysteresis Model Parameters Estimation using GABogomir Zidaric0Damijan Miljavec1TECESUniverza v LjubljaniThis paper presents the Jiles and Atherton (J-A) hysteresis model parameter estimation for soft magnetic composite (SMC) material. The calculation of Jiles and Atherton hysteresis model parameters is based on experimental data and genetic algorithms (GA). Genetic algorithms operate in a given area of possible solutions. Finding the best solution of a problem in wide area of possible solutions is uncertain. A new approach in use of genetic algorithms is proposed to overcome this uncertainty. The basis of this approach is in genetic algorithm built in another genetic algorithm.http://advances.utc.sk/index.php/AEEE/article/view/261hysteresis modelmagnetic compositejiles and atherton hysteresis model.
spellingShingle Bogomir Zidaric
Damijan Miljavec
J-A Hysteresis Model Parameters Estimation using GA
Advances in Electrical and Electronic Engineering
hysteresis model
magnetic composite
jiles and atherton hysteresis model.
title J-A Hysteresis Model Parameters Estimation using GA
title_full J-A Hysteresis Model Parameters Estimation using GA
title_fullStr J-A Hysteresis Model Parameters Estimation using GA
title_full_unstemmed J-A Hysteresis Model Parameters Estimation using GA
title_short J-A Hysteresis Model Parameters Estimation using GA
title_sort j a hysteresis model parameters estimation using ga
topic hysteresis model
magnetic composite
jiles and atherton hysteresis model.
url http://advances.utc.sk/index.php/AEEE/article/view/261
work_keys_str_mv AT bogomirzidaric jahysteresismodelparametersestimationusingga
AT damijanmiljavec jahysteresismodelparametersestimationusingga