Neural network meta-modeling and optimization of flux switching machines

Kurtovic H, Hahn I (2019)

Publication Type: Conference contribution

Publication year: 2019

Publisher: Institute of Electrical and Electronics Engineers Inc.

Pages Range: 629-636

Conference Proceedings Title: 2019 IEEE International Electric Machines and Drives Conference, IEMDC 2019

Event location: San Diego, CA US

ISBN: 9781538693490

DOI: 10.1109/IEMDC.2019.8785344

Abstract

This paper presents the application of neural networks (NN)in the design and optimization of the flux switching machine. A finite element (FE)model of the flux switching machine is used to create data for the training of NNs. The trained NN meta-models are used to predict the properties of machine designs. Subsequently, a preselection from these predictions for further FE calculations is employed. Based on this approach, a generational NN based optimization is implemented. Furthermore, during the optimization many NN topologies and output variable combinations are investigated. The NN predictions and their quality are evaluated according to output variables, generations, and NN layouts. In addition, the improvements in the machine's capabilities are presented using a Pareto-domination based approach.

Authors with CRIS profile

Haris Kurtovic Professur für Elektrische Antriebe und Maschinen Ingo Hahn Professur für Elektrische Antriebe und Maschinen

How to cite

APA:

Kurtovic, H., & Hahn, I. (2019). Neural network meta-modeling and optimization of flux switching machines. In 2019 IEEE International Electric Machines and Drives Conference, IEMDC 2019 (pp. 629-636). San Diego, CA, US: Institute of Electrical and Electronics Engineers Inc..

MLA:

Kurtovic, Haris, and Ingo Hahn. "Neural network meta-modeling and optimization of flux switching machines." Proceedings of the 11th IEEE International Electric Machines and Drives Conference, IEMDC 2019, San Diego, CA Institute of Electrical and Electronics Engineers Inc., 2019. 629-636.

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