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J4  2012, Vol. 46 Issue (5): 873-877    DOI: 10.3785/j.issn.1008-973X.2012.05.016
自动化技术、电气工程     
贝叶斯证据框架下LS-SVM的BPMSM磁链建模
孙晓东,陈龙,杨泽斌,朱熀秋,嵇小辅
江苏大学 汽车工程研究院, 电气信息工程学院, 江苏 镇江 212013
Modeling of flux linkage for the BPMSM based on LS-SVM within the
Bayesian evidence framework
SUN Xiao-dong, CHEN Long, YANG Ze-bin, ZHU Huang-qiu, JI Xiao-fu
Automotive Engineering Research Institute, School of Electrical and Information Engineering,
Jiangsu University, Zhenjiang, 212013, China
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摘要:

针对常规解析法建立无轴承永磁同步电机(BPMSM)磁链模型的局限性,提出一种贝叶斯证据框架下最小二乘支持向量机(LS-SVM)的BPMSM磁链建模方法.对BPMSM磁链的非线性建模进行简单分析,在介绍LS-SVM回归理论和贝叶斯证据框架基本思想的基础上,通过贝叶斯证据框架推断准则1确定模型的权向量w,通过贝叶斯证据框架推断准则2确定模型的正则化参数γ,通过贝叶斯证据框架推断准则3确定模型的核参数σ,进而建立基于贝叶斯证据框架下LS-SVM的BPMSM磁链模型.在Matlab7.0环境下进行仿真研究.仿真结果表明,贝叶斯证据框架下LS-SVM的磁链模型具有拟合精度高、泛化能力强、结构灵活、计算速度快等特点.

Abstract:

 According to the  modeling of the flux linkage model for bearingless permanent magnet synchronous motors (BPMSMs) by using the conventional analytic method, a novel modeling method based on least squares support vector machine (LS-SVM) within the Bayesian evidence framework was proposed. Fistly, the nonlinear modeling of the flux linkage model  was analyzed briefly. Secondly, the regression theory of the LS-SVM and the basic concept of the Bayesian evidence framework (namely, the three levels inference) were introduced in detail. And then the level 1 inference was used to determine the weight vector w of the LS-SVM. The level 2 inference was used to ascertain the model regularization parameter γ of the LS-SVM. The level 3 inference was used to obtain the kernel parameter σ of the LS-SVM, and thus the flux linkage model of the BPMSM based on LS-SVM within the Bayesian evidence framework was established. Finally, the simulation studies were carried out with the Matlab7.0 software to illustrate the performance of the proposed method. Simulation results show that this model has high accurate precision, good generalization ability, flexible structure, and rapid calculation speed.

出版日期: 2012-05-01
:  TM 341  
基金资助:

国家“863”高科技研究发展计划资助项目(2007AA04Z213);国家自然科学基金资助项目(61104016, 51105177);江苏省高校自然科学研究面上资助项目(11KJB510002);江苏省研究生科研创新计划基金项目(CX10B_270Z);高等学校博士学科点专项科研基金(20113227120015);江苏高校优势学科建设工程资助项目(苏政办发\[2011\]6号);江苏大学高级人才科研启动基金项目(11JDG047).

作者简介: 孙晓东(1981-),男,讲师,从事无轴承电机,交流电机控制,非线性智能控制等方向研究.E-mail: xdsun@ujs.edu.cn
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引用本文:

孙晓东,陈龙,杨泽斌,朱熀秋,嵇小辅. 贝叶斯证据框架下LS-SVM的BPMSM磁链建模[J]. J4, 2012, 46(5): 873-877.

SUN Xiao-dong, CHEN Long, YANG Ze-bin, ZHU Huang-qiu, JI Xiao-fu. Modeling of flux linkage for the BPMSM based on LS-SVM within the
Bayesian evidence framework. J4, 2012, 46(5): 873-877.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2012.05.016        http://www.zjujournals.com/eng/CN/Y2012/V46/I5/873

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