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基于支持向量机的设备振动信号趋势预测 被引量：4

Trend Forecasting of Mechanical Equipment Vibration Signal Based on SVM
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摘要 详细分析了支持向量机用于趋势预测的理论基础,通过Lorenz仿真信号的单步、多步预测证明,支持向量机由于采用了结构风险最小化准则表现出优秀的推广能力,预测区间较长且精度较高.工程应用实例也表明支持向量机在设备趋势预测中可以满足实际需要,具有很好的应用前景. This paper introduces the basic theory of SVM in time series forecasting. Then, Lorenz signal is predicted via SVM. The result shows that the superiority of this method is due to the adoption of new type of structural risk minimization principle.The real forecasting of vibration signal demonstrates the promising application of SVM.
作者 侯澍旻 李友荣 刘光临
机构地区 武汉科技大学机械自动化学院
出处 《湖北工业大学学报》 2006年第3期12-14,17,共4页 Journal of Hubei University of Technology
关键词 趋势预测 支持向量机 振动信号 trend forecasting supports vector machine vibration signal
分类号 TH17 [机械工程—机械制造及自动化]
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参考文献4

  • 1[1]Vladimir N.Vapnik,Nature of Statistics Learning Theory[M].New York:Springer-verlag,2000.
  • 2[2]Tay F E H,Cao L J.Application of Support Vector Machines in Financial Forecasting[J].Omega,2001,9(4):309-317.
  • 3[3]Nello Cristianini,John Shawe Taylor.支持向量机导论[M].北京:电子工业出版社,2004:15-40.
  • 4刘隽,周涛,周佩玲.GA优化支持向量机用于混沌时间序列预测[J].中国科学技术大学学报,2005,35(2):258-263. 被引量：21

二级参考文献15

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共引文献25

同被引文献33

引证文献4

二级引证文献19

湖北工业大学学报
2006年 第3期

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