Modeling of Core Loss Based on Machine Learning and Deep Learning Article Swipe

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Junqi He , Yifeng Wei , Daiguang Jin · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2502.05487

This article proposes a Mix Neural Network (MNN) based on CNN-FCNN for predicting magnetic loss of different materials. In traditional magnetic core loss models, empirical equations usually need to be regressed under the same external conditions. When the magnetic core material is different, it needs to be classified and discussed. If external factors increase, multiple models need to be proposed for classification and discussion, making the modeling process extremely cumbersome. And traditional empirical equations still has the problem of low accuracy, although various correction equations have been introduced later, the accuracy has always been unsatisfactory. By introducing machine learning and deep learning, it is possible to simultaneously solve prediction problems with low accuracy of empirical equations and complex conditions. Based on the MagNet database, through the training of the newly proposed MNN, it is found that a single model is sufficient to make predictions for at least four different materials under varying temperatures, frequencies, and waveforms, with accuracy far exceeding that of traditional models. At the same time, we also used three other machine learning and deep learning models (Random Forest, XGBoost, MLP-LSTM) for training, all of which had much higher accuracy than traditional models. On the basis of the predicted results, a hybrid model combining MNN and XGBoost was proposed, which predicted through weighting and found that the accuracy could continue to improve. This provides a solution for modeling magnetic core loss under different materials and operating modes.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2502.05487

PDF

https://arxiv.org/pdf/2502.05487

OA Status

green

Related Works

10

OpenAlex ID

https://openalex.org/W4407384919

Raw OpenAlex JSON

OpenAlex ID

https://openalex.org/W4407384919

Canonical identifier for this work in OpenAlex

DOI

https://doi.org/10.48550/arxiv.2502.05487

Digital Object Identifier

Title

Modeling of Core Loss Based on Machine Learning and Deep Learning

Work title

Type

preprint

OpenAlex work type

Language

en

Primary language

Publication year

2025

Year of publication

Publication date

2025-02-08

Full publication date if available

Authors

Junqi He, Yifeng Wei, Daiguang Jin

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Landing page

https://arxiv.org/abs/2502.05487

Publisher landing page

PDF URL

https://arxiv.org/pdf/2502.05487

Direct link to full text PDF

Open access

Yes

Whether a free full text is available

OA status

green

Open access status per OpenAlex

OA URL

https://arxiv.org/pdf/2502.05487

Direct OA link when available

Concepts

Core (optical fiber), Artificial intelligence, Computer science, Deep learning, Machine learning, Telecommunications

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Cited by

0

Total citation count in OpenAlex

Related works (count)

10

Other works algorithmically related by OpenAlex

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