Open AccessChaos modeling and real-time online prediction of permanent magnet synchronous motor based on multiple kernel least squares support vector machine
Author(s) -
Qiang Chen,
Xuemei Ren
Publication year - 2010
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.59.2310
Subject(s) - chaotic , support vector machine , computer science , kernel (algebra) , least squares support vector machine , control theory (sociology) , noise (video) , kernel method , least squares function approximation , algorithm , series (stratigraphy) , artificial intelligence , mathematics , statistics , paleontology , control (management) , combinatorics , estimator , image (mathematics) , biology
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.