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柔性支持向量回归及其在故障检测中的应用

易辉 宋晓峰 姜斌 刘宇芳 周智华

易辉, 宋晓峰, 姜斌, 刘宇芳, 周智华. 柔性支持向量回归及其在故障检测中的应用. 自动化学报, 2013, 39(3): 272-284. doi: 10.3724/SP.J.1004.2013.00272
引用本文: 易辉, 宋晓峰, 姜斌, 刘宇芳, 周智华. 柔性支持向量回归及其在故障检测中的应用. 自动化学报, 2013, 39(3): 272-284. doi: 10.3724/SP.J.1004.2013.00272
YI Hui, SONG Xiao-Feng, JIANG Bin, LIU Yu-Fang, ZHOU Zhi-Hua. Flexible Support Vector Regression and Its Application to Fault Detection. ACTA AUTOMATICA SINICA, 2013, 39(3): 272-284. doi: 10.3724/SP.J.1004.2013.00272
Citation: YI Hui, SONG Xiao-Feng, JIANG Bin, LIU Yu-Fang, ZHOU Zhi-Hua. Flexible Support Vector Regression and Its Application to Fault Detection. ACTA AUTOMATICA SINICA, 2013, 39(3): 272-284. doi: 10.3724/SP.J.1004.2013.00272

柔性支持向量回归及其在故障检测中的应用

doi: 10.3724/SP.J.1004.2013.00272
详细信息
    通讯作者:

    姜斌

Flexible Support Vector Regression and Its Application to Fault Detection

  • 摘要: 支持向量回归(Support vector regression, SVR)的学习性能及泛化性能取决于参数设置.在常规方法中,这些参数以固定值形式参与运算,而当面对复杂分布的数据集时, 可能无法挑选出一组能够胜任各种分布情况的参数,参数设置需要在过拟合和欠拟合之间进行取舍. 因此,本文提出一种能够根据样本分布进行参数自我调整的柔性支持向量回归算法(Flexible support vector regression, F-SVR).该算法根据样本分布的复杂度,将训练样本划分为多个区域,在训练过程中, F-SVR为不同 区域设置不同的训练参数,有效避免了过拟合与欠拟合.本文首先采用一组人工数据对所提算法有效性进行验证,在实验中, F-SVR在 保持学习能力的同时,具备较传统方法更优秀的泛化性能.最后,本文将该算法运用至高频电源故障的实际检测,效果良好.
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出版历程
  • 收稿日期:  2011-09-15
  • 修回日期:  2012-10-14
  • 刊出日期:  2013-03-20

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