Design of permanent magnet drive based on improved support vector regression

被引：1

作者：

Li, Zhao ^{[1
]}

Wang, Da-Zhi ^{[1
]}

机构：

[1] School of Information Science & Engineering, Northeastern University, Shenyang,110819, China

来源：

Dongbei Daxue Xuebao/Journal of Northeastern University | 2017年 / 38卷 / 02期

关键词：

Particle swarm optimization (PSO) - Vector spaces - Vectors - Multiobjective optimization - Regression analysis;

D O I：

10.3969/j.issn.1005-3026.2017.02.002

中图分类号：

学科分类号：

摘要：

The multi-output support vector regression with composite kernel and the fuzzy theory were applied to design permanent magnet drive. In this method, the space particle swarm optimization (SPSO) algorithm was firstly introduced to obtain the most appropriate parameter of the multi-output support vector regression with composite kernel model. In addition, through the experiment the regression model between performances and structure parameters of permanent magnet drive was established. Secondly, by using fuzzy theory, multi-objective problem was converted into single one, and the mathematical model of optimization problem was set up, which was solved by SPSO. Finally, precision analysis of model, ANSYS simulation and prototyping test were carried out, and the results verified the effectiveness of the proposed method. © 2017, Editorial Department of Journal of Northeastern University. All right reserved.

引用

页码：158 / 162