离散数据拟合模型的研究与实现  

Research and implementation of discrete data fitting model

作　　者：尹文怡[1] 范通让[1] 

机构地区：[1]石家庄铁道学院计算机与信息工程分院,石家庄050043

出　　处：《计算机工程与应用》2008年第31期227-228,共2页Computer Engineering and Applications

基　　金：河北省自然科学基金No.F2006000101~~

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。Least Squares Support Vector Machines （LS-SVM） with small samples.This paper approaches a data fitting model is a novel support vector machines for discrete date fitting even 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.

关 键 词：离散数据 数据拟合 最小二乘支持向量机 曲线拟合 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论]