An improved Kriging surrogate model method with high robustness for electrical machine optimization-Reference-Cited by-同舟云学术

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An improved Kriging surrogate model method with high robustness for electrical machine optimization

Published:2022-10-28 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific)
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Author:

Zhang Junli¹,Hua Wei²,Gao Yuan³,Wang Yuchen²,Zhang Hengliang²

Affiliation:

1. Southeast University,The College of Software Engineering,Nanjing,China

2. Southeast University,School of Electrical Engineering,Nanjing,China

3. University of Bristol,Department of Aerospace Engineering,Bristol,UK

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9941506/9941741/09942076.pdf?arnumber=9942076

Reference12 articles.

1. Robust Design Optimization of a Five-Phase PM Hub Motor for Fault-Tolerant Operation Based on Taguchi Method

2. Kriging Surrogate Model-Based Design of an Ultra-High-Speed Surface-Mounted Permanent-Magnet Synchronous Motor Considering Stator Iron Loss and Rotor Eddy Current Loss

3. Research on Swarm Size of Multi-swarm Particle Swarm Optimization Algorithm

4. Robust Design Optimization of Electrical Machines: Multi-Objective Approach

5. Optimization of Three-Phase Induction Motor Design Using Simulated Annealing Algorithm

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Large-scale Optimization with 3D FEA Using Online Machine Learning Technique for Axial Flux Machine;2023 IEEE Energy Conversion Congress and Exposition (ECCE);2023-10-29

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