Numerical Modeling of Suspension Force for Bearingless Flywheel Machine Based on Differential Evolution Extreme Learning Machine

Zhiying Zhu, Jin Zhu, Xuan Guo, Yongjiang Jiang and Yukun Sun
Additional contact information
Zhiying Zhu: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Jin Zhu: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Xuan Guo: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Yongjiang Jiang: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Yukun Sun: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China

Energies, 2019, vol. 12, issue 23, 1-17

Abstract: The analytical model (AM) of suspension force in a bearingless flywheel machine has model mismatch problems due to magnetic saturation and rotor eccentricity. A numerical modeling method based on the differential evolution (DE) extreme learning machine (ELM) is proposed in this paper. The representative input and output sample set are obtained by finite-element analysis (FEA) and principal component analysis (PCA), and the numerical model of suspension force is obtained by training ELM. Additionally, the DE algorithm is employed to optimize the ELM parameters to improve the model accuracy. Finally, absolute error (AE) and root mean squared error (RMSE) are introduced as evaluation indexes to conduct comparative analyses with other commonly-used machine learning algorithms, such as k-Nearest Neighbor (KNN), the back propagation (BP) algorithm, and support vector machines (SVMs). The results show that, compared with the above algorithm, the proposed method has smaller fitting and prediction errors; the RMSE value is just 22.88% of KNN, 39.90% of BP, and 58.37% of SVM, which verifies the effectiveness and validity of the proposed numerical modeling method.

Keywords: numerical model; principal component analysis; differential evolution; extreme learning machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/23/4470/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/23/4470/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:23:p:4470-:d:290317

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().