Description: The module LSQ is for unconstrained linear least-squares fitting. It is
based upon Applied Statistics algorithm AS 274 (see comments at the start
of the module). A planar-rotation algorithm is used to update the QR-
factorization. This makes it suitable for updating regressions as more
data become available. The module contains a test for singularities which
is simpler and quicker than calculating the singular-value decomposition.
An important feature of the algorithm is that it does not square the condition
number. The matrix X X is not formed. Hence it is suitable for ill-
conditioned problems, such as fitting polynomials.
By taking advantage of the MODULE facility, it has been possible to remove
many of the arguments to routines. Apart from the new function VARPRD,
and a back-substitution routine BKSUB2 which it calls, the routines behave
as in AS 274.-The module is for unconstrained linear least-squares fitting. It is based upon Applied Statistics algorithm AS 274 (see comments at the start of the module). A planar - rotation algorithm is used to update the QR-factorization. This makes it suitable for updating regressions as more data become available. The module contains a test for singularities which is simpler and quicker than calculating the singular-value decomposition. An important feature of the algorithm is that it does not square the condition number. The matrix X X is not formed. Hence it is suitable for ill-conditioned problems, such as fitting Polynomials. By taking advantage of the MODULE facility, it has been possible to remove many of the arguments to routines. Apart from the new function VARPRD, and a back - substitution Platform: |
Size: 57867 |
Author:AiQing |
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Description: The module LSQ is for unconstrained linear least-squares fitting. It is
based upon Applied Statistics algorithm AS 274 (see comments at the start
of the module). A planar-rotation algorithm is used to update the QR-
factorization. This makes it suitable for updating regressions as more
data become available. The module contains a test for singularities which
is simpler and quicker than calculating the singular-value decomposition.
An important feature of the algorithm is that it does not square the condition
number. The matrix X X is not formed. Hence it is suitable for ill-
conditioned problems, such as fitting polynomials.
By taking advantage of the MODULE facility, it has been possible to remove
many of the arguments to routines. Apart from the new function VARPRD,
and a back-substitution routine BKSUB2 which it calls, the routines behave
as in AS 274.-The module is for unconstrained linear least-squares fitting. It is based upon Applied Statistics algorithm AS 274 (see comments at the start of the module). A planar- rotation algorithm is used to update the QR-factorization. This makes it suitable for updating regressions as more data become available. The module contains a test for singularities which is simpler and quicker than calculating the singular-value decomposition. An important feature of the algorithm is that it does not square the condition number. The matrix X X is not formed. Hence it is suitable for ill-conditioned problems, such as fitting Polynomials. By taking advantage of the MODULE facility, it has been possible to remove many of the arguments to routines. Apart from the new function VARPRD, and a back- substitution Platform: |
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Author: |
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Description: 用Fortran语言实现的多种插值算法和最小二乘拟合算法，有源代码，有简要介绍。-Fortran language with a variety of interpolation algorithms and least squares fitting algorithm, active code, have a brief introduction. Platform: |
Size: 2048 |
Author:木子 |
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Description: 曲线拟合的java算法。本代码是采用数学中的最小二乘法原理编写的。-Curve fitting algorithm of java. This code is the use of mathematics in the preparation of the principle of least square method. Platform: |
Size: 2048 |
Author:肖剑 |
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Description: 强局部加权回归算法由Cleveland[7]提出,主要利用局部观测数据对欲拟合点进行多项式加权拟合,并用最小二乘法进行估计.它综合了传统的局部多项式拟合,局部加权回归以及具有强鲁棒性的拟合过程
-Strong locally weighted regression algorithm by Cleveland [7] proposed, mainly using local observational data points on the polynomial fitting For weighted fitting, and estimated by least square method. It combines the traditional local polynomial fitting, locally weighted regression as well as strong robustness of the fitting process Platform: |
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Author:wanghuaqiu |
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Description: 最小二乘拟合算法C程序，N为拟合点数，T为拟合阶数。-Least-squares fitting algorithm C procedures, N for the fitting points, T for the fitting order. Platform: |
Size: 1024 |
Author:唐荣 |
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Description: 利用最小二乘算法对一组坐标进行曲线拟合，其中包含了利用直接LU分解法解方程的算法代码实现。希望对用到数值计算的带来帮助。-The least square algorithm for fitting a set of coordinates, including the use of direct LU decomposition equations for the algorithm code to achieve. Want to use to bring numerical help. Platform: |
Size: 103424 |
Author:风流云散 |
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Description: 用最小二乘法，在已知函数点x0，x1……xn的函数值y0，y1……yn的情况下，求拟合多项式-The least square method, the known function points x0, x1 ... ... xn of the function values y0, y1 ... ... yn circumstances, find fitting polynomial Platform: |
Size: 1024 |
Author:陈炎龙 |
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Description: 在VC++6.0环境下编写的一个最小二乘算法，可以对任意幂数的五个点进行曲线拟合-Written in vc++ 6.0 environment，it is a least-square algorithm of arbitrary power, it can the number of curve fitting five points
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Author:周静 |
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Description: 最小 二乘拟合算法。移植性性一般不过也可以互相学学的-The least square fitting algorithm. Portability is generally but also can learn each other I
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Author:郜琳琳 |
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Description: Matlab实现最小二乘拟合.要求输入拟合点，输出拟合函数的系数并在同一坐标系下画出离散点和拟合曲线。-matlab has given a least square fitting algorithm.and given combine curve. Platform: |
Size: 14336 |
Author:李平 |
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Description: 递推最小二乘拟合算法 用于实时拟合时间序列ARMA模型参数 例如 陀螺仪随机噪声 股票 交通等模型的参数拟合(Recursive least square fitting algorithm is used to fit the parameters of time series ARMA model, such as gyroscope, random noise, stock traffic and so on) Platform: |
Size: 12288 |
Author:btty
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Description: 对于单输入单输出的系统（Single input single output，SISO）常采用最小二乘方法辨识系统的参数。最小二乘参数估计是一个经典的方法，概念简明，适应范围广，来源于数理统计的回归分析，它能提供一个在最小方差意义上与实验数据最好拟合的模型，在一些情况下，可得到与极大似然法一样好的统计效果，并能很方便地与其它辨识算法建立关系。在一定条件下，最小二乘法参数估计法有最佳的统计特性，即一致的、无偏的和有效的结果。本代码主要关于使用递推最小二乘辨识方法与增广最小二乘辨识方法辨识模型参数，采用高斯噪声作为系统的噪声。(For Single input Single output (SISO), the least squares method is used to identify the parameters of the system.
Least squares parameter estimation is a classic method, concept is concise, wide adaptation, derived from the regression analysis of mathematical statistics, it can provide a minimum variance sense the best fitting model with the experimental data, in some cases, the statistics can be obtained with the maximum likelihood method is as good effect, and can easily establish relations with other identification algorithm.
Under certain conditions, the least square parameter estimation method has the best statistical properties, namely consistent, unbiased and effective results.
This code mainly USES the method of recursive least squares identification method and the augmented least squares identification method to identify the model parameters, using gaussian noise as the noise of the system.) Platform: |
Size: 1024 |
Author:M.R.J.
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Description: 利用基于最小二乘法的多项式拟合算法处理商业数据，包含源码(Using the polynomial fitting algorithm based on the least square method to deal with commercial data, including the source code) Platform: |
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Author:hgfji |
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Search - Least Square Fitting algorithm