Influence of Hyperparameters Choice for Neural Flux Linkage Model of Synchronous Machines
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F23%3A43969716" target="_blank" >RIV/49777513:23220/23:43969716 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Influence of Hyperparameters Choice for Neural Flux Linkage Model of Synchronous Machines
Original language description
A proper choice of hyperparameters is crucial in machine learning approaches. In this paper, we investigate the influence of hyperparameters of a neural flux linkage model on a quality of this model and model’s prediction capabilities. Specifically, we compare several different setups for number of hidden layers and number of neurons in these layers. The universal NeuralODE architecture is used for the neural flux linkage model. The results are validated on a real data of IPMSM.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů