Artificial neural network modeling of mean-field ising hysteresis

In this study, the artificial neural network (ANN) was used to model ferromagnetic Ising hysteresis obtained from mean-field analysis as a case study. ANNs were trained to predict the effect of external perturbations, which are the temperature, the field amplitude and the field frequency, on the hys...

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Main Authors: Laosiritaworn W., Laosiritaworn Y.
Format: Conference or Workshop Item
Language:English
Published: 2014
Online Access:http://www.scopus.com/inward/record.url?eid=2-s2.0-66549120802&partnerID=40&md5=7e4fd6e5285a934171d95b9c1edcfe34
http://cmuir.cmu.ac.th/handle/6653943832/1444
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Institution: Chiang Mai University
Language: English
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spelling th-cmuir.6653943832-14442014-08-29T09:29:18Z Artificial neural network modeling of mean-field ising hysteresis Laosiritaworn W. Laosiritaworn Y. In this study, the artificial neural network (ANN) was used to model ferromagnetic Ising hysteresis obtained from mean-field analysis as a case study. ANNs were trained to predict the effect of external perturbations, which are the temperature, the field amplitude and the field frequency, on the hysteresis properties, which are the hysteresis area, the remanence magnetization and the coercivity. The input data to the ANN were split into training data, testing data and validating data. Search were carried out to identify number of hidden layer and number of hidden nodes to find the best architecture with highest accuracy. After the networks had been trained, they were used to predict hysteresis properties of the unseen testing patterns of input. The predicted and the actual data were found to match very well over an extensive range. This therefore suggests a success in modeling ferromagnetic hysteresis properties using the ANN technique. © 2009 IEEE. 2014-08-29T09:29:18Z 2014-08-29T09:29:18Z 2009 Conference Paper 00189464 10.1109/TMAG.2009.2018940 IEMGA http://www.scopus.com/inward/record.url?eid=2-s2.0-66549120802&partnerID=40&md5=7e4fd6e5285a934171d95b9c1edcfe34 http://cmuir.cmu.ac.th/handle/6653943832/1444 English
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
language English
description In this study, the artificial neural network (ANN) was used to model ferromagnetic Ising hysteresis obtained from mean-field analysis as a case study. ANNs were trained to predict the effect of external perturbations, which are the temperature, the field amplitude and the field frequency, on the hysteresis properties, which are the hysteresis area, the remanence magnetization and the coercivity. The input data to the ANN were split into training data, testing data and validating data. Search were carried out to identify number of hidden layer and number of hidden nodes to find the best architecture with highest accuracy. After the networks had been trained, they were used to predict hysteresis properties of the unseen testing patterns of input. The predicted and the actual data were found to match very well over an extensive range. This therefore suggests a success in modeling ferromagnetic hysteresis properties using the ANN technique. © 2009 IEEE.
format Conference or Workshop Item
author Laosiritaworn W.
Laosiritaworn Y.
spellingShingle Laosiritaworn W.
Laosiritaworn Y.
Artificial neural network modeling of mean-field ising hysteresis
author_facet Laosiritaworn W.
Laosiritaworn Y.
author_sort Laosiritaworn W.
title Artificial neural network modeling of mean-field ising hysteresis
title_short Artificial neural network modeling of mean-field ising hysteresis
title_full Artificial neural network modeling of mean-field ising hysteresis
title_fullStr Artificial neural network modeling of mean-field ising hysteresis
title_full_unstemmed Artificial neural network modeling of mean-field ising hysteresis
title_sort artificial neural network modeling of mean-field ising hysteresis
publishDate 2014
url http://www.scopus.com/inward/record.url?eid=2-s2.0-66549120802&partnerID=40&md5=7e4fd6e5285a934171d95b9c1edcfe34
http://cmuir.cmu.ac.th/handle/6653943832/1444
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