Multi-objective optimization based on hyperparameter random forest regression for linear motor design-Reference-Cited by-同舟云学术

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Multi-objective optimization based on hyperparameter random forest regression for linear motor design

Published:2022-05-26 Issue:10 Volume:13 Page:2929-2942
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Wen Cheng,Zhao Qiankai,Li Mingye,Liu Jingna,Li Mingwei,Zhao Xingqiao

Funder

National Natural Science Foundation of China

Natural Science Foundation of China’s Hebei Province

Young Top-notch Talents project of Education Department of China’s Hebei Province

postgraduates innovation fund of Shijiazhuang Tiedao University

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-022-01573-z.pdf

Reference49 articles.

1. Duan Y, Dan M (2013) A review of recent developments in electrical machine design optimization methods with a permanent-magnet synchronous motor benchmark study. IEEE Trans Ind Appl 49(3):1268–1275

2. Huang X, Li T et al (2019) Detent force, thrust, and normal force of the short-primary double-sided permanent magnet linear synchronous motor with slot-shift structure. IEEE Trans Ener Conv 34(3):1411–1421

3. Tan Q, Huang X, Li L, Wang M (2019) Analysis of flux linkage and detent force for a modular tubular permanent magnet synchronous linear motor with large slots. IEEE Trans Energy Convers 34(3):1532–1541

4. Min SG, Sarlioglu B (2018) 3-D performance analysis and multi-objective optimization of coreless-type PM linear synchronous motors. IEEE Trans Ind Electron 65(2):1855–1864

5. Lv G, Zeng D, Zhou T, Degano M (2021) A complete equivalent circuit for linear induction motors with laterally asymmetric secondary for urban railway transit. IEEE Trans Ener Conv 36(2):1014–1022

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