中文摘要：

磁悬浮开关磁阻电机（BSRM）的电感矩阵是电机建模的基础，本文提出了基于最小二乘支持向量机（LS-SVM）的电机电感辨识建模方法。首先通过对BSRM电感特性的有限元分析，获得各参数对电感的影响规律，然后结合LS-SVM在有限样本数据下对高维非线性的逼近能力，离线建立BSRM各种运行工况下的电感模型。另外在建模中，针对LS-SVM超参数选取问题，采用粒子群优化算法（PSO）对其进行自动寻优，以提高电感模型精度。最后通过对比仿真研究，表明PSO-15-SVM模型能够准确反映电机磁饱和下的电感特性，这为BSRM磁饱和模型的构建奠定了基础。

英文摘要：

The inductance matrix is very important for the model of bearingless switched reluctance motors （BSRM）. A novel modeling method of the inductance for BSRM using least squares-support vector machine （LS-SVM） was presented. First, the inductance characteristic of BSRM was analyzed by the finite elements method （FEM）. For the nonlinear character of the inductance, this approach takes advantage of LS-SVM with better solution for small-sample learning problem and good generalization ability. Through the off-line learning, a better LS-SYM was built to form an efficient nonlinear mapping for the inductance mode of BSRM. Then, the particle swarm optimization （PSO） ~algorithm was used to optimize parameters of LS-SVM to improve the accuracy of the inductance model. Finally, the comparative simulation research showed that the PSO-LS-SVM model could accurately reflect the inductance characteristics of BSRM under magnetic satu- ration. This makes a contribution to the model of BSRM considering the characteristic of magnetic saturation.