A smoothing algorithm using cubic spline functions
A smoothing algorithm using cubic spline functions
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使用三次样条函数的平滑算法
DOI：
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发表时间：
1974
期刊：
影响因子：
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通讯作者：
L. M. Howser
中科院分区：
文献类型：
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作者：
R. Smith;J. Price;L. M. Howser
提出了两种平滑任意数据集的算法。它们分别是显式变量算法和参数变量算法。前者将用于由于所需的计算量较小而不会遇到大梯度的情况。如果要平滑的数据是双值的或经历较大的梯度，则使用后者。这两种算法都使用最小二乘技术来获得数据的三次样条拟合。样条拟合的优点是一阶导数和二阶导数是连续的。这种方法最好在交互式图形环境中使用，以便可以操纵样条曲线的连接点值以改善拟合。
Two algorithms are presented for smoothing arbitrary sets of data. They are the explicit variable algorithm and the parametric variable algorithm. The former would be used where large gradients are not encountered because of the smaller amount of calculation required. The latter would be used if the data being smoothed were double valued or experienced large gradients. Both algorithms use a least-squares technique to obtain a cubic spline fit to the data. The advantage of the spline fit is that the first and second derivatives are continuous. This method is best used in an interactive graphics environment so that the junction values for the spline curve can be manipulated to improve the fit.