On-line modeling by curve-fitting
On-line modeling by curve-fitting
复制标题
通过曲线拟合进行在线建模
DOI：
--
复制
发表时间：
1972
期刊：
影响因子：
--
通讯作者：
R. Shrager
中科院分区：
文献类型：
--
作者：
G. Knott;R. Shrager
建模，正如我们在这里使用的术语，意味着将从某种理论获得的一组数字与实验室获得的一组数字相匹配。我们将把实验数据称为观测数据或曲线，把理论数据称为计算数据或曲线。
我们的目的可能是评估模型;或者用它来确定否则无法获得的值作为观察曲线的函数。因此，曲线拟合建模的关键问题是参数的确定。模型函数包含未知或模糊已知的称为参数的数字，这些参数影响计算曲线的形状。问题是要找到那些参数值，使计算曲线最相似的观察曲线在最小二乘意义上。这就是为什么我们说通过曲线拟合建模。统计学家称这种行为为非线性回归。
Modeling, as we use the term here, means matching a set of numbers, obtained from some theory, to a set of numbers from the laboratory. We will refer to the laboratory numbers as the observed data or curve and to the theoretical numbers as the computed data or curve.
Our purpose may be to evaluate the model; or to use it to determine otherwise unobtainable values as functions of the observed curve. Thus the key problem in modeling by curve-fitting is parameter determination. The model functions contain unknown or vaguely known numbers called parameters that affect the shape of the computed curve. The problem is to find those parameter values that make the computed curve most resemble the observed curve in the least squares sense. This is why we speak of modeling by curve-fitting. Statisticians call this activity non-linear regression.