Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (31): 227-228.DOI: 10.3778/j.issn.1002-8331.2008.31.066

• 工程与应用 • Previous Articles     Next Articles

Research and implementation of discrete data fitting model

YIN Wen-yi,FAN Tong-rang


  1. Department of Computer and Information Engineering,Shijiazhuang Railway Institute,Shijiazhuang 050043,China
  • Received:2008-05-27 Revised:2008-08-06 Online:2008-11-01 Published:2008-11-01
  • Contact: YIN Wen-yi



  1. 石家庄铁道学院 计算机与信息工程分院,石家庄 050043
  • 通讯作者: 尹文怡

Abstract: Least Squares Support Vector Machines(LS-SVM) is a novel support vector machines for discrete date fitting even with small samples.This paper approaches a data fitting model based on the LS-SVM.Motor characteristic curves are fitted with the intelligent model.The results show that the model has excellent learning ability and generalization ability,and can provide more accurate data fitting curve only with few observed samples compared with least square method.

Key words: discrete data, data fitting, Least Squares Support Vector Machines(LS-SVM), curve fitting

摘要: 最小二乘支持向量机引入到离散数据拟合中,代替传统的最小二乘法解决离散数据拟合问题。推导了用于函数估计的最小二乘支持向量机算法,构建了基于最小二乘支持向量机的离散数据拟合模型,并对电机数据拟合进行了研究。结果表明,最小二乘支持向量机拟合离散数据比最小二乘法精度更高、拟合效果更好。

关键词: 离散数据, 数据拟合, 最小二乘支持向量机, 曲线拟合