摘要
In the present paper,a new criterion is derived to obtain the optimum fitting curve while using Cubic B-spline basis functions to remove the statistical noise in the spectroscopic data.In this criterion,firstly,smoothed fitting curves using Cubic B-spline basis functions are selected with the increasing knot number.Then,the best fitting curves are selected according to the value of the minimum residual sum of squares(RSS)of two adjacent fitting curves.In the case of more than one best fitting curves,the authors use Reinsch's first condition to find a better one.The minimum residual sum of squares(RSS)of fitting curve with noisy data is not recommended as the criterion to determine the best fitting curve,because this value decreases to zero as the number of selected channels increases and the minimum value gives no smoothing effect.Compared with Reinsch's method,the derived criterion is simple and enables the smoothing conditions to be determined automatically without any initial input parameter.With the derived criterion,the satisfactory result was obtained for the experimental spectroscopic data to remove the statistical noise using Cubic B-spline basis functions.
In the present paper, a new criterion is derived to obtain the optimum fitting curve while using Cubic B-spline basis functions to remove the statistical noise in the spectroscopic data. In this criterion, firstly, smoothed fitting curves using Cubic B-spline basis functions are selected with the increasing knot number. Then, the best fitting curves are selected according to the value of the minimum residual sum of squares （RSS） of two adjacent fitting curves. In the case of more than one best fitting curves, the authors use Reinsch＇s first condition to find a better one. The minimum residual sum of squares （RSS） of fitting curve with noisy data is not recommended as the criterion to determine the best fitting curve, because this value decreases to zero as the number of selected channels increases and the minimum value gives no smoothing effect. Compared with Reinsch＇s method, the derived criterion is simple and enables the smoothing conditions to be determined automatically without any initial .input parameter. With the derived criterion, the satisfactory result was obtained for the experimental spectroscopic data to remove the statistical noise using Cubic B-spline basis functions.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2009年第10期2721-2724,共4页
Spectroscopy and Spectral Analysis
基金
Supported by the Science and Technology Development Fund of Macao(China)grant(No.042/2007/A3,No.003/2008/A1)
partly supported by NSFC Project(No.10631080)
National Key Basic Research Project of China grant(No.2004CB318000)
关键词
分光镜
光化学
机械滑动
花键函数
Spectroscopic data
Automatic smoothing
Spline function
