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

Lozito, Gabriele Maria; Quercio, Michele; Sabino, Lorenzo; Laudani, Antonino

doi:10.1109/icem60801.2024.10700401

2024 International Conference on Electrical Machines (ICEM) 2024

DOI: 10.1109/icem60801.2024.10700401

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

Gabriele Maria Lozito,

Michele Quercio,

Lorenzo Sabino

et al.

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Cited by 1 publication

References 38 publications

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Neural Network Architectures and Magnetic Hysteresis: Overview and Comparisons

Licciardi,

Ala,

Francomano

et al. 2024

Mathematics

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The development of innovative materials, based on the modern technologies and processes, is the key factor to improve the energetic sustainability and reduce the environmental impact of electrical equipment. In particular, the modeling of magnetic hysteresis is crucial for the design and construction of electrical and electronic devices. In recent years, additive manufacturing techniques are playing a decisive role in the project and production of magnetic elements and circuits for applications in various engineering fields. To this aim, the use of the deep learning paradigm, integrated with the most common models of the magnetic hysteresis process, has become increasingly present in recent years. The intent of this paper is to provide the features of a wide range of deep learning tools to be applied to magnetic hysteresis context and beyond. The possibilities of building neural networks in hybrid form are innumerable, so it is not plausible to illustrate them in a single paper, but in the present context, several neural networks used in the scientific literature, integrated with various hysteretic mathematical models, including the well-known Preisach model, are compared. It is shown that this hybrid approach not only improves the modeling of hysteresis by significantly reducing computational time and efforts, but also offers new perspectives for the analysis and prediction of the behavior of magnetic materials, with significant implications for the production of advanced devices.

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Neural Network Architectures and Magnetic Hysteresis: Overview and Comparisons

Licciardi,

Ala,

Francomano

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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

Cited by 1 publication

References 38 publications

Neural Network Architectures and Magnetic Hysteresis: Overview and Comparisons

Neural Network Architectures and Magnetic Hysteresis: Overview and Comparisons

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