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- W4292388483 abstract "This paper presents a method for inductance calculation of coils based on a machine learning algorithm. To show the feasibility of the approach, we generate a set of artificial training data describing a configuration of two planar spiral coils in varying dimensions and positions to each other in order to calculate their self- and mutual inductance. Afterwards, the data is used to train and evaluate three different machine learning models. Our evaluation shows that multiple linear regression with polynomial features reaches almost the same precision as the reference FASTHENRY2, but is orders of magnitude faster. With this novel machine learning based algorithm we enable new applications, where real-time prediction of inductances or coupling factors is advantageous." @default.
- W4292388483 created "2022-08-20" @default.
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- W4292388483 date "2022-07-20" @default.
- W4292388483 modified "2023-09-28" @default.
- W4292388483 title "Feasibility Study on Machine Learning-based Method for Determining Self-and Mutual Inductance" @default.
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- W4292388483 doi "https://doi.org/10.1109/isitia56226.2022.9855321" @default.
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