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基于径向基函数的变量预测模型模式识别方法

潘海洋 杨宇 郑近德 程军圣

潘海洋, 杨宇, 郑近德, 程军圣. 基于径向基函数的变量预测模型模式识别方法[J]. 航空动力学报, 2017, 32(2): 500-506. doi: 10.13224/j.cnki.jasp.2017.02.031
引用本文: 潘海洋, 杨宇, 郑近德, 程军圣. 基于径向基函数的变量预测模型模式识别方法[J]. 航空动力学报, 2017, 32(2): 500-506. doi: 10.13224/j.cnki.jasp.2017.02.031
Variable predictive model based RBF class discriminate method[J]. Journal of Aerospace Power, 2017, 32(2): 500-506. doi: 10.13224/j.cnki.jasp.2017.02.031
Citation: Variable predictive model based RBF class discriminate method[J]. Journal of Aerospace Power, 2017, 32(2): 500-506. doi: 10.13224/j.cnki.jasp.2017.02.031

基于径向基函数的变量预测模型模式识别方法

doi: 10.13224/j.cnki.jasp.2017.02.031
基金项目: 国家重点研发计划(2016YFF0203400); 国家自然科学基金(51575168,51375152);智能型新能源汽车国家2011协同创新中心资助项目; 湖南省绿色汽车2011协同创新中心资助项目

Variable predictive model based RBF class discriminate method

  • 摘要: 针对变量预测模型模式识别方法中4种数学模型不足以反映特征值之间复杂关系的缺陷.因此,提出了一种基于径向基函数的变量预测模型(VPMRBF)模式识别方法,把提取的特征值输入到VPMRBF分类器中,然后通过训练样本建立反映特征值之间复杂关系的径向基函数预测模型,最后把测试样本的特征值作为径向基函数预测模型的输入,以预测误差平方和为依据完成分类.该方法充分有效地利用并且结合径向基函数和变量预测模式识别方法的优点,实现了故障特征提取到故障识别的全程诊断. 滚动轴承故障诊断实验分析结果表明:与径向基神经网络、支持向量机和变量预测模式识别方法相比,VPMRBF的识别率分别提高了4.75%,1.75%和5.25%.

     

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出版历程
  • 收稿日期:  2015-05-19
  • 刊出日期:  2017-02-28

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