Surrogate-Based Modeling of Induction Machines to Reduce the Computational Burden of Robust Multi-Objective Optimization-Reference-Cited by-同舟云学术

Surrogate-Based Modeling of Induction Machines to Reduce the Computational Burden of Robust Multi-Objective Optimization

Published:2023-05 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Magnetic Conference (INTERMAG)
language:
Short-container-title:

Author:

Taqavi Omolbanin¹,Fatima Areej¹,Bourgault Alex¹,Li Ze¹,Byczynski Glenn²,Tjong Jimi¹,Kar Narayan C.¹

Affiliation:

1. University of Windsor,Centre for Hybrid Automotive Research and Green Energy (CHARGE),Windsor,ON,Canada,N9B 3P4

2. Nemak,Windsor,ON,Canada,N9C 4G8

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10264999/10265056/10265080.pdf?arnumber=10265080

Reference13 articles.

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3. A Computationally efficient surrogate model based robust optimization for permanent magnet synchronous machines;yang;IEEE Trans Energy Convers,2022

4. Dataset on optimization of EDM machining parameters by using central composite design

5. Robust-Oriented Optimization of Switched Reluctance Motors Considering Manufacturing Fluctuation

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