Machine-learning Aided Multi-objective Optimization of Electric Machines - Geometric-feasibility and Enhanced Regression Models

This paper deals with the optimization of electrical machines by means of ANN-based classification and regression models. Geometrically (or otherwise) unfeasible designs are detected with high accuracy during the optimization process by means of an ad hoc classification model, whereas continuous tar...

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Veröffentlicht in:	IEEE journal of emerging and selected topics in industrial electronics (Print) 2023-03, p.1-11
Hauptverfasser:	Pop, Adrian-C., Cai, Zhaofeng, Gyselinck, Johan J. C.
Format:	Artikel
Sprache:	eng
Schlagworte:	artificial neural network (ANN) classification models Computational modeling constrained multi-objective design optimization Electric machines Iron machine-learning (ML) Neurons Optimization permanent-magnet synchronous machines (PMSMs) Predictive models surrogate modeling Torque Training
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