Performance prediction of switched reluctance motor using improved generalized regression neural networks for design optimization
Since practical mathematical model for the design optimization of switched reluctance motor (SRM) is difficult to derive because of the strong nonlinearity, precise prediction of electromagnetic characteristics is of great importance during the optimization procedure. In this paper, an improved gene...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
China Electrotechnical Society
2018-12-01
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| Series: | CES Transactions on Electrical Machines and Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8598255 |
| Summary: | Since practical mathematical model for the design optimization of switched reluctance motor (SRM) is difficult to derive because of the strong nonlinearity, precise prediction of electromagnetic characteristics is of great importance during the optimization procedure. In this paper, an improved generalized regression neural network (GRNN) optimized by fruit fly optimization algorithm (FOA) is proposed for the modeling of SRM that represent the relationship of torque ripple and efficiency with the optimization variables, stator pole arc, rotor pole arc and rotor yoke height. Finite element parametric analysis technology is used to obtain the sample data for GRNN training and verification. Comprehensive comparisons and analysis among back propagation neural network (BPNN), radial basis function neural network (RBFNN), extreme learning machine (ELM) and GRNN is made to test the effectiveness and superiority of FOA-GRNN. |
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| ISSN: | 2096-3564 2837-0325 |