Neural network modeling of magnetic hysteresis

Akbarzadeh, Vahab; Davoudpour, Maryam; Sadeghian, Alireza

doi:10.1109/etfa.2008.4638563

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Their work described the behavior of ferromagnetic materials in the rolling and transverse direction of an isotropic material [21]. Akharzadeh et al showed that NNs can be used to predict the satisfactory features of some observed hysteric behavior and therefore NNs can be used for many interesting applications [22].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Hysteresis Loop of Barium Hexaferrite Nanoparticles Based on Neuroevolutionary Models

et al. 2021

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Neuroevolutionary models are used to predict magnetic hysteresis for barium hexaferrites (to predict magnetic hysteresis for barium hexaferrites). Magnetic hysteresis for a specific set of samples of barium hexaferrite doped with titanium were measured experimentally at room temperature and reported before. Neural networks are trained using these experimental data in order to generate magnetization and predict magnetic hysteresis for various concentrations of titanum. We present the prediction for various methods of neural calculations and the deviations from actual data results were negligible. Finally, the predictions of magnetic hysteresis are summerized for the titanume concentration between 0.0 and 1.0.

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Section: Introductionmentioning

confidence: 99%

Prediction of Hysteresis Loop of Barium Hexaferrite Nanoparticles Based on Neuroevolutionary Models

et al. 2021

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“…The function can then be applied in software using a look-up table of values. Other 'black-box' identification techniques used alongside the Preisach model include genetic algorithms [10], fuzzy models [11] and artificial neural networks (ANN) [12][13][14]. In [15], Saliah et al…”

Section: Introductionmentioning

confidence: 99%

Dynamic Ferromagnetic Hysteresis Modelling Using a Preisach-Recurrent Neural Network Model

et al. 2020

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In this work, a Preisach-recurrent neural network model is proposed to predict the dynamic hysteresis in ARMCO pure iron, an important soft magnetic material in particle accelerator magnets. A recurrent neural network coupled with Preisach play operators is proposed, along with a novel validation method for the identification of the model’s parameters. The proposed model is found to predict the magnetic flux density of ARMCO pure iron with a Normalised Root Mean Square Error (NRMSE) better than 0.7%, when trained with just six different hysteresis loops. The model is evaluated using ramp-rates not used in the training procedure, which shows the ability of the model to predict data which has not been measured. The results demonstrate that the Preisach model based on a recurrent neural network can accurately describe ferromagnetic dynamic hysteresis when trained with a limited amount of data, showing the model’s potential in the field of materials science.

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A Comparative Analysis on Different Deep Neural Network Models for Magnetic Hysteresis with Distorted Excitation Waveforms

Lozito,

Quercio,

Sabino

et al. 2024

2024 International Conference on Electrical Machines (ICEM)

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Neural network modeling of magnetic hysteresis

Cited by 3 publications

References 7 publications

Prediction of Hysteresis Loop of Barium Hexaferrite Nanoparticles Based on Neuroevolutionary Models

Prediction of Hysteresis Loop of Barium Hexaferrite Nanoparticles Based on Neuroevolutionary Models

Dynamic Ferromagnetic Hysteresis Modelling Using a Preisach-Recurrent Neural Network Model

A Comparative Analysis on Different Deep Neural Network Models for Magnetic Hysteresis with Distorted Excitation Waveforms

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