An Improved Moving Least Squares Method for Curve and Surface Fitting

被引：7

作者：

Zhang, Lei ^{[1
]}

Gu, Tianqi ^{[1
]}

Zhao, Ji ^{[1
]}

Ji, Shijun ^{[1
]}

Hu, Ming ^{[1
]}

Li, Xiangbo ^{[1
]}

机构：

[1] Jilin Univ, Coll Mech Sci & Engn, Changchun 130025, Peoples R China

来源：

MATHEMATICAL PROBLEMS IN ENGINEERING | 2013年 / 2013卷

基金：

高等学校博士学科点专项科研基金;

关键词：

APPROXIMATION;

D O I：

10.1155/2013/159694

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The moving least squares (MLS) method has been developed for the fitting of measured data contaminated with random error. The local approximants of MLS method only take the error of dependent variable into account, whereas the independent variable of measured data always contains random error. Considering the errors of all variables, this paper presents an improved moving least squares (IMLS) method to generate curve and surface for the measured data. In IMLS method, total least squares (TLS) with a parameter lambda based on singular value decomposition is introduced to the local approximants. A procedure is developed to determine the parameter lambda. Numerical examples for curve and surface fitting are given to prove the performance of IMLS method.

引用

页数：6

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