期刊
ENERGIES
卷 12, 期 23, 页码 -出版社
MDPI
DOI: 10.3390/en12234470
关键词
numerical model; principal component analysis; differential evolution; extreme learning machine
资金
- National Natural Science Foundation of China [51977103, 51877101]
- Postdoctoral Science Foundation Funded Project of China [2018M632201]
- Six Talent Peaks Project of Jiangsu [GDZB-026]
- Postgraduate Research & Practice Innovation Program of Jiangsu Province [SJCX18_0565, SJCX19_0525]
- College Students Science and Technology Innovation Fund of Nanjing Institute of Technology [TZ20190031]
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.
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