摘要
针对传统的移动最小二乘法在非均匀分布的采样点集拟合中的不足,提出了影响域半径动态调整的移动最小二乘法(RSRMLS)。在传统移动最小二乘法(MLS)的基础上,根据拟合子区域采样点数据稀疏情况,该方法可自动调整MLS的半径区域大小。通过对相同数据点集的拟合比较,提出的RSRMLS拟合效果明显优于传统MLS。
In order to handle the non-uniform sampling points fitting,this paper presents an adaptive adjustment of the radius of influence domain with the moving least squares（RSRMLS）.Based on the moving least squares（MLS） fitting,this method can automatically adjust the size of radius for MLS according to the consistency of the sampled data points.This approach is compared with traditional MLS on the same sampling points,and this method has more approximate fitting results than the traditional MLS.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第35期153-156,共4页
Computer Engineering and Applications
基金
浙江省重大科技专项No.2007C11022~~
关键词
影响域半径动态调整的移动最小二乘法(RSRMLS)
影响域半径
拟合
K-近邻
adaptive adjustment of the radius of influence domain with the moving least squares（RSRMLS）
radius of influence domain
fitting
k-nearest neighbors
