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支持向量回归预测模型在材料性能预测中的应用 被引量:1

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摘要 与材料性能相关的预测模型,可以实现优化工艺,减少试验次数,节约研究时间和研究经费。本文介绍了支持向量回归原理,并以3种材料为例,介绍基于支持向量回归的预测模型对材料性能的预测。研究实例结果表明:支持向量回归预测模型具有良好的学习和泛化能力。研究者可以通过基于支持向量回归预测模型对各种材料的性能进行预测。
作者 唐江凌 黄健
出处 《科技视界》 2015年第17期42-43,61,共3页 Science & Technology Vision
基金 广西高校科研项目(YB 2014471)
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参考文献6

  • 1Z.Lu,J.Sun.Non-Mercer hybrid kernel for linear programming support vector regression in nonlinear systems identification[J].Applied Soft Computing,2009,9(1):94-99.
  • 2K.W.Lau,Q.H.Wu.Local prediction of non-linear time series using support vector regression[J].Pattern Recognition,2008,41(5):1539-1547.
  • 3B.Schǒlkopf,A.J.Smola.Learning with kernels[M].1st edition.London:The MIT press,2002:25-60.
  • 4唐江凌,蔡从中,皇思洁,肖婷婷.Al-Cu-Mg-Ag合金强度性能的支持向量回归预测[J].航空材料学报,2012,32(5):92-96. 被引量:5
  • 5唐江凌,蔡从中,肖婷婷,皇思洁.支持向量回归在Zr-2合金晶粒尺寸预测中的应用[J].材料热处理学报,2013,34(2):180-184. 被引量:2
  • 6J.L.Tang C.Z.Cai,et al.Modeling and Predicting Tensile Strength of Tungsten Alloy by Using PSO-SVR[J].Advanced Materials Research,2012,455-456:1497-1503.

二级参考文献28

  • 1梁桂兆,李志良,周原,何留,周鹏.一种新多肽表征方法及支持向量机用于肽HPLC定量结构-保留建模预测[J].物理化学学报,2006,22(9):1052-1055. 被引量:3
  • 2刘文庆,雷鸣,耿迅,李强,周邦新.显微组织对Zr-Sn-Nb-Fe锆合金耐腐蚀性能的影响[J].材料热处理学报,2006,27(6):47-51. 被引量:16
  • 3丁益民,张婷婷,刘旭,陈念贻.CsF-RbF二元系相图[J].物理化学学报,2007,23(4):614-616. 被引量:1
  • 4Vapnik V. The Nature of Statistical Learning Theory [M]. New York : Springer, 1995.
  • 5Liong S Y, Sivapragasam C. Flood stage forecasting with support vector machines [ J ]. Journal of the American Water Resources Association,2002,38 (1) :173 -186.
  • 6Gavrish V V, Ganguli S B. Support vector machines as an efficient tool for high-dimensional data processing : Application to sub-storm forecasting [ J ]. Journal of Geophysical Research-Space Physics ,2001,106 ( A12 ) :29911 - 29914.
  • 7Hua S J, Ssn Z R. A novel method of protein secondary structure prediction with high segment overlap measure : support vector machine approach [ J ]. Journal of Molecular Biology,2001,308 (2) :397 - 407.
  • 8Cai C Z,Han L Y, Ji Z L, et al. SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence [ J ]. Nucleic Acids Research,2003,31:3692 - 3697.
  • 9Cai C Z, Han L Y, Ji Z L, et al. Enzyme family classification by support vector machines[ J ]. Proteins,2004 55:66 -76.
  • 10Wen Y F,Cai C Z,Liu X H et al. Corrosion rate prediction of 3C steel under different seawater environment based on support vector regression [J]. Corrosion Science, 2009,51 ( 2 ) : 349 - 355.

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