|本期目录/Table of Contents|

[1]司利云,林辉. 基于支持向量机的开关磁阻电机转子位置在线建模[J].电机与控制学报,2014,18(04):72-78.
 SI Li-yun,LIN Hui. Online modeling based on support vector machine for rotor positionsignal of switched reluctance motors[J].,2014,18(04):72-78.
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 基于支持向量机的开关磁阻电机转子位置在线建模(PDF)
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《电机与控制学报》[ISSN:1007-449X/CN:23-1 408/TM]

卷:
18
期数:
2014年04
页码:
72-78
栏目:
出版日期:
2014-04-15

文章信息/Info

Title:
 Online modeling based on support vector machine for rotor position
signal of switched reluctance motors
作者:
 司利云 林辉
 1. 长安大学电子与控制工程学院,陕西西安710064; 2. 西北工业大学自动化学院,陕西西安710072
Author(s):
 SI Li-yun LIN Hui
 (1. School of Electrical and Control Engineering,Chang’an University,Xi’an 710064,China;
2. School of Automation,Northwestern Polytechnical University,Xi’an 710072,China
关键词:
 开关磁阻电机 无位置传感器 在线建模 支持向量机 灰色关联
Keywords:
 switch reluctance motors sensorless online modeling support vector machine grey correlation
分类号:
-
DOI:
-
文献标志码:
A
摘要:
 针对开关磁阻电机离线转子位置估计器在实际工程应用中可能存在预测精确度变差的问
题,提出了一种基于近似支持向量回归机的在线转子位置预测模型。根据支持向量回归机在线建
模的原理,充分考虑电机的实际运行状况,结合在线预测模型对收敛速度与预测精确度的要求,提
出了基于灰色关联的加权分类近似支持向量回归机模型。该模型由于只需求解线性方程组,具有
计算简单快速的优点。基于灰色关联度的在线动态训练样本集的选取由于考虑了样本的时间与空
间特性,使得模型具有良好的泛化性。实验结果表明: 只需较小的训练样本集,便可实现电机转子
位置的准确估计。
Abstract:
 For off-line rotor position estimation accuracy of switched reluctance motor ( SRM) in practical
application might become poor,online rotor position prediction model based on proximal support vector
regression ( PSVR) is proposed. According to the online modeling principle of SVR,fully considering the
motor actual operation condition,combined with the convergence speed and prediction accuracy requirements
of online prediction model,weighted proximal SVR machine via classification ( WCPSVR) based
on grey correlation is presented. This model only needs to solve linear equations,with the advantages of
simple and fast calculation. Online dynamic set of training samples based on grey correlation degree was
selected taking into account the samples’characteristics of time and space,so it made the model has a
good generalization. Experimental results show that this on-lining prediction model realizes accurate prediction
of rotor position using a smaller set of training samples.

参考文献/References:

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备注/Memo

备注/Memo:
更新日期/Last Update: 2014-11-25