基于多核最小二乘支持向量机的永磁同步电机混沌建模及其实时在线预测

提出了多核最小二乘支持向量机的永磁同步电机混沌系统建模方法. 通过不同核函数的线性加权组合构造新的等价核，降低建模精度对核函数及其参数选择的依赖性. 理论上给出多核最小二乘支持向量机回归参数和模型输出值的求解方法. 采用关联积分计算方法对永磁同步电机混沌系统进行相空间重构，以窗式移动的在线学习方式对重构后的永磁同步电机混沌序列进行一步和多步实时在线预测，并讨论了不同测量噪声对该方法的影响. 仿真结果表明，该方法能有效提高永磁同步电机混沌系统的建模精度，具有良好的抗噪能力. 关键词： 永磁同步电机 多核学习 最小二乘支持向量机 混沌预测

Chaos modeling and real-time online prediction of permanent magnet synchronous motor based on multiple kernel least squares support vector machine

A multiple kernel least squares support vector machine (MK-LSSVM) modeling method is proposed for the chaos of permanent magnet synchronous motor (PMSM). An equivalent kernel is built by linear-weighted combination of multi kernels to reduce the dependence of modeling accuracy on kernel function and parameters. The solutions of regression parameters and MK-LSSVM output are given in theory. C-C method is employed for the phase space reconstruction of PMSM chaos, then one-step and multi-step real-time online prediction of reconstructed chaotic series are investigated based on moving window learning method. The effect of different measurement noises on the proposed method is discussed. Simulations show that the proposed method can enhance the modeling accuracy and have strong anti-noise capability.