CAJ | 학술논문

探索单绕组磁悬浮开关磁阻电机的结构优化设计，研究了一种基于极限学习机与粒子群的设计方法。该方法在有限元仿真分析的基础上给出各结构参数对悬浮力影响的一般规律，并选择定子极弧、转子极弧作为优化对象，利用仿真数据进行极限学习机的模型训练，给出了样本空间设计，对模型的训练效果进行了定性定量的描述，并与支持向量机进行了训练效果对比，最后选用粒子群算法对电机训练模型进行寻优以提高悬浮力输出。结果表明，基于极限学习机的训练模型精度高、模型回归速度快，粒子群算法能快速准确地寻取最优解。
탐색단요조자현부개관자조전궤적결구우화설계，연구료일충기우겁한학습궤여입자군적설계방법。해방법재유한원방진분석적기출상급출각결구삼수대현부력영향적일반규률，병선택정자겁호、전자겁호작위우화대상，이용방진수거진행겁한학습궤적모형훈련，급출료양본공간설계，대모형적훈련효과진행료정성정량적묘술，병여지지향량궤진행료훈련효과대비，최후선용입자군산법대전궤훈련모형진행심우이제고현부력수출。결과표명，기우겁한학습궤적훈련모형정도고、모형회귀속도쾌，입자군산법능쾌속준학지심취최우해。
To realize the optimization design of the single winding bearingless switched reluctance motor, a design method is studied using extreme learning machine and particle swarm optimization algorithm. Firstly, general rules of effects of various structure parameters on radial forces are given based on finite element analysis, and stator pole arc and rotor pole arc are selected accordingly for optimization. Then nonlinear regression model is trained by extreme learning machine with the data from the finite element simulation. Besides, sample space is presented, and effects of the trained model are showed qualitatively and quantitatively, and then comparisons with support vector machine are made. Finally, the particle swarm optimization algorithm is used to search for the optimal solution. The results prove that the nonparametric model has good precision and fast speed of regression, and the particle swarm optimization algorithm is able to find the optimal solution quickly and accurately.