On Modeling the Dynamic Thermal Behavior of Electrical Machines Using Genetic Programming and Artificial Neural Networks-Reference-Cited by-同舟云学术

On Modeling the Dynamic Thermal Behavior of Electrical Machines Using Genetic Programming and Artificial Neural Networks

Published:2020 Issue: Volume: Page:319-326
ISSN:0302-9743
Container-title:Computer Aided Systems Theory – EUROCAST 2019
language:
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Author:

Zăvoianu Alexandru-Ciprian,Kitzberger Martin,Bramerdorfer Gerd,Saminger-Platz Susanne

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-45093-9_39

Reference15 articles.

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3. Bramerdorfer, G., et al.: Using FE calculations and data-based system identification techniques to model the nonlinear behavior of PMSMs. IEEE Trans. Ind. Electron. 61(11), 6454–6462 (2014)

4. Bramerdorfer, G., Zăvoianu, A.C.: Surrogate-based multi-objective optimization of electrical machine designs facilitating tolerance analysis. IEEE Trans. Mag. 53(8), 1–11 (2017)

5. Eibe, F., Hall, M., Witten, I.: The WEKA Workbench. Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Online Appendix for Data Mining (2016)

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