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An Improved Moving Least Squares Method for Curve and Surface Fitting

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  • Lei Zhang
  • Tianqi Gu
  • Ji Zhao
  • Shijun Ji
  • Ming Hu
  • Xiangbo Li

Abstract

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 based on singular value decomposition is introduced to the local approximants. A procedure is developed to determine the parameter . Numerical examples for curve and surface fitting are given to prove the performance of IMLS method.

Suggested Citation

  • Lei Zhang & Tianqi Gu & Ji Zhao & Shijun Ji & Ming Hu & Xiangbo Li, 2013. "An Improved Moving Least Squares Method for Curve and Surface Fitting," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-6, November.
    • Handle: RePEc:hin:jnlmpe:159694
    DOI: 10.1155/2013/159694
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    Cited by:

    1. Xinyi Qiu & Sung-Ho Kil & Hyun-Kil Jo & Chan Park & Wonkyong Song & Yun Eui Choi, 2023. "Cooling Effect of Urban Blue and Green Spaces: A Case Study of Changsha, China," IJERPH, MDPI, vol. 20(3), pages 1-14, February.

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