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最小二乘法直线拟合程序
- 此程序为vc程序源代码，而且是多项式拟合程序，具体做法是从一副图上读出离散或连续的点，拟合成直线或曲线。-procedure for vc source code, but is polynomial fitting procedures, the specific approach is a map read out discrete or continuous, straight line or be synthetic curve.
hpir10
- 最小二乘曲线拟合算法（用最小二乘法求给定数据点的拟合多项式）-least squares curve fitting algorithm (with the least-squares method for the given data points of polynomial fitting)
curvefit_C_edition
- c语言版的多项式曲线拟合。 用最小二乘法进行曲线拟合. 用p－1 次多项式进行拟合，p<= 10 x,y 的第0个域x[0],y[0],没有用，有效数据从x[1],y[1] 开始 nNodeNum,有效数据节点的个数。 b,为输出的多项式系数，b[i] 为b[i-1]次项。b[0],没有用。 b,有10个元素ok。-c language version of the polynomial curve fitting. Using least-squares met
2x
- 利用最小二乘法进行曲线的拟合,这是用多项式拟合曲线的源码!-using the least-squares method of curve fitting, which is the polynomial curve fitting source!
polyfit
- 曲线拟合程序 多项式相关系数的计算方法(多项式形式1) 多项式相关系数的计算方法(多项式形式2) 最小二乘法曲线拟合 三次样条插值（自然边界条件）-polynomial curve fitting procedures correlation coefficient is calculated (the form of a polynomial) polynomial coefficient of correlation Methods (polynomial form 2
Correlation1
- //=== === === === === === ===== //函数说明 //函数名称：Correlation //函数功能：计算最小二乘法拟合的多项式的相关系数 //使用方法：int M------ 拟合多项式的阶数（已知条件） // double *b--- 拟合曲线的系数,排列顺序为由高阶到低阶（已知条件） // double *x--- 结点x轴数据（已知条件） // double *y--- 结点y轴数据（已知条件） // double
srir
- 最小二乘法——一般多项式拟合曲线，并以x-eexp(-x) 0<=x<=2 ,为例进行拟合-Least square method- general polynomial fitting curve, and x-eexp (-x) 0 < = x < = 2, as an example, fitting
curvefitting
- 采用最小二乘法的三次多项式对离散数据点进行曲线拟合 -least-squares approximation curve fitting
curvefit
- 用最小二乘法进行曲线拟合. 用p－1 次多项式进行拟合，p<= 10 x,y 的第0个域x[0],y[0],没有用，有效数据从x[1],y[1] 开始 nNodeNum,有效数据节点的个数。 b,为输出的多项式系数，b[i] 为b[i-1]次项。b[0],没有用。 b,有10个元素ok-Using least squares curve fitting. With p-1 order polynomial fit, p <= 10 x, y 0 of
yiyuanduo
- 用最小二乘法进行曲线拟合，一次多项式拟合f(x),可稍作改动进行多项式拟合-Using the least square method to curve fitting, a polynomial fitting f (x) can be slightly modified to polynomial fitting
polynomial-fitting
- 基于BCB的最小二乘法进项曲线拟合，最后得出多项式方程，并显示出图形-Method of least squares curve fitting proceeds
BestFitting
- 使用java实现最小二乘算法，实现曲线拟合，实现多项式拟合(Using java algorithm to achieve the least squares, curve fitting, polynomial fitting)
最小二乘法拟合曲线C语言代码
- 用最下二乘法多项式进行曲线拟合进而插值。(With the least two multiplicative polynomial for curve fitting, and then interpolation.)
正交多项式最小二乘拟合
- 计算给定点列的曲线拟合最小二乘法得到的函数(The function of least square method for curve fitting of fixed point column)
多项式最小二乘拟合与龙贝格积分法
- 通过最小二乘法拟合曲线，并使用龙贝格公式计算积分(By the method of least squares fitting curve, and use the formula to calculate the Romberg integral)
fit_analysis
- 最小二乘法多项式曲线拟合，根据给定的m个点,并不要求这条曲线精确地经过这些点，而是曲线y=f(x)的近似曲线。(The least squares polynomial curve fitting, according to the given M point, does not require the curve to pass precisely these points, but rather the approximate curve of the curve y=f (x).)
Language
- 程序实现线性插值、抛物插值、牛顿多项式插值、等距节点插值、最小二乘法的曲线拟合对函数进行近似(The function is approximated by linear interpolation, parabolic interpolation, Newton polynomial interpolation, equidistant node interpolation, and least square curve fitting.)
实验2
- 最小二乘法详细程序实现。曲线拟合。 1.直线型 2.多项式型 3.分数函数型 4.指数函数型 5.对数线性型 6.高斯函数型(The principle of least square method, formula deduction, program realization.)
最小二乘法分段直线拟合
- 曲线拟合是图像分析中非常重要的描述符号。最常用的曲线拟合方法是最小二乘法,然而一般的最小二乘法有一定的局限性,已经有不少学者对其进行了一些改进。进一步对最小二乘法进行改进,提出一种新的分段直线拟合算法来代替多项式曲线拟合,以达到简化数学模型的建立和减少计算的目的,使其能够更好地对点序列进行拟合。(Curve fitting is a very important descr iptor in image analysis,the most commonly used curve fitting
曲线拟合
- 最小二乘法实现曲线拟合，返回拟合多项式系数(Least Squares Method for Curve Fitting)