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DC Field | Value | Language |
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dc.contributor.author | Grech, Christian | - |
dc.contributor.author | Buzio, Marco | - |
dc.contributor.author | Pentella, Mariano | - |
dc.contributor.author | Sammut, Nicholas | - |
dc.date.accessioned | 2020-07-17T07:26:23Z | - |
dc.date.available | 2020-07-17T07:26:23Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Grech, C., Buzio, M., Pentella, M., & Sammut, N. (2020). Dynamic ferromagnetic hysteresis modelling using a preisach-recurrent neural network model. Materials, 13(11), 2561. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/58860 | - |
dc.description.abstract | 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. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | MDPI AG | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Machine learning | en_GB |
dc.subject | Metals -- Magnetic properties | en_GB |
dc.subject | Particle accelerators | en_GB |
dc.subject | Ferromagnetic materials | en_GB |
dc.subject | Hysteresis | en_GB |
dc.subject | Piezoelectric devices | en_GB |
dc.title | Dynamic ferromagnetic hysteresis modelling using a preisach-recurrent neural network model | en_GB |
dc.type | article | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.3390/ma13112561 | - |
dc.publication.title | Materials | en_GB |
Appears in Collections: | Scholarly Works - FacICTMN |
Files in This Item:
File | Description | Size | Format | |
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materials-13-02561.pdf | 4.24 MB | Adobe PDF | View/Open |
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