中文摘要：

针对永磁同步电机在一定情况下呈现混沌特性且混沌模型难以精确获得的情况,提出了一种基于多核对称最小二乘支持向量机的回归建模方法.在最小二乘支持向量机模型中增加对称性的约束条件,构成对称最小二乘支持向量机.将多核学习的方法与对称最小二乘支持向量机相结合,构造由多个基本核函数线性组合而成的新的等价核,用于建立永磁同步电机的混沌回归模型.仿真结果表明,与一般最小二乘支持向量机相比,该方法能够降低单个核函数的选择对建模精度的影响,提高混沌建模精度.

英文摘要：

In this paper,a multiple kernel symmetric least squares support vector machine（MKSLSSVM） regression modeling method is proposed for the case that chaotic characteristics are displayed in the permanent magnet synchronous motors（PMSM） under certain circumstances and the exact chaotic model is difficult to obtain.A symmetric constraint condition is added to the least squares support vector machine（LSSVM） model to construct the symmetric LSSVM（SLSSVM）.Then,SLSSVM is integrated with multiple kernel learning technique to form a novel equivalent kernel,which is composed of linear combination of multi basic kernels.This novel equivalent kernel can be employed for the chaotic modeling of PMSM.Simulation results show that,compared with LSSVM,the proposed scheme can reduce the effect of modeling error caused by selecting of kernel function and enhance the chaos modeling accuracy.