%0 Journal Article
%T Application of Moving Least Squares to Multi-sensors Data Reconstruction<br>移动最小二乘法在多功能传感器数据重构中的应用
%A LIU Dan
%A SUN Jin-Wei
%A WEI Guo
%A LIU Xin
%A <br>刘丹
%A 孙金玮
%A 魏国
%A 刘昕
%J 自动化学报
%D 2007
%I 
%X In this paper,a novel numerical solution method,moving least squares (MLS),is employed to solve the problem of nonlinear reconstruction of multi-functional sensor with a view that least squares (LS) are restricted in global regression.Through studying the construction method and characters of interpolated function,basis function and weight function are selected reasonably to obtain the coefficients in trial function,and the reconstructed value of input signals is acquired.This paper presents an analysis of the effects of the parameters in MLS,such as the dimensions of basis function,the number of points in the support domain,and the coefficient of weight function.Comparisons are made between LS and MLS reconstruction data,whose relative errors are smaller than 15.3% and 1.03%,respectively.The results demonstrate that MLS is suitable for nonlinear regression of curves.Additionally,more points in the support domain or higher dimensions of basis function will greatly increase the reconstructed accuracy.
%K Least squares
%K moving least squares
%K nonlinear regression of curve
%K data reconstruction<br>最小二乘法
%K 移动最小二乘
%K 非线性曲面拟合
%K 数据重构
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=83CFB18E8FFD212E&yid=A732AF04DDA03BB3&vid=27746BCEEE58E9DC&iid=5D311CA918CA9A03&sid=CE504F5B1E192581&eid=E0FF0FB27B45F84E&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=8