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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

  • DOI - Digital Object Identifier

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

  • Czech description

Classification

  • Type

    O - Miscellaneous

  • CEP classification

  • OECD FORD branch

    20201 - Electrical and electronic engineering

Result continuities

  • Project

  • 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