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偏最小二乘回归的线性与非线性方法，王惠文著。讲了偏最小二乘通径回归模型和递阶偏最小二乘回归模型。(Partial Least Squares Regression Linear and Nonlinear Methods, by Wang Huiwen. The partial least square path regression model and the hierarchical partial least-squares regression model are introduced.
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Jacobi iteration for solving linear equations class-based, Least-squares regression analysis algorithm, Interpolation and fitting matlab implementation.
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线性回归是利用称为线性回归方程的最小二乘函数对一个或多个自变量和因变量之间关系进行建模的一种回归分析(Linear regression is a regression analysis based on the least squares function of linear regression equation, which is used to model the relationship between one or more independent variables and dep
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进行数值计算的著名软件，LAPACK包含了求解科学与工程计算中最常见的数值线性代数问题，如求解线性方程组、线性最小二乘问题、特征值问题和奇异值问题等。(LAPACK is written in Fortran 90 and provides routines for solving systems of simultaneous linear equations, least-squares solutions of linear systems of equations, eigenvalue
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Functions
kalman_filter
kalman_smoother - implements the RTS equations
learn_kalman - finds maximum likelihood estimates of the parameters using EM
sample_lds - generate random samples
AR_to_SS - convert Auto Regressive model of order k to State
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ALGLIB is a cross-platform numerical analysis and data processing library. It supports several programming languages (C++, C#, Delphi) and several operating systems (Windows and POSIX, including Linux). ALGLIB features include:
Data analysis (clas
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最小二乘法的系统参数辨识函数，可以辨识任意的线性函数，效果比其他的一般的最小二乘法好(The system parameter identification function of least squares method can identify any linear function, and the effect is better than other ordinary least squares method.)
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最速下降法就是梯度下降法,可以用于求解最小二乘问题(线性和非线性都可以)。可以说是求解机器算法的最古老、最经典的模型，另一种常用的方法是最小二乘法。(The steepest descent method is the gradient descent method, which can be used to solve the least squares problem (both linear and non-linear). It can be said that it is the ol
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一般的线性方程我们可以用最小二乘来解，一般的非线性方程我们可以用LM来解。
这里是线性微分方程组，所以我们采用最小二乘来解。
关键是构造出最小二乘形式，微分可以通过前后数据差分的方法来求。
不过这里还有一个技巧就是如果数据前后帧间隔过大，可以先插值，再对插值后的数据差分如果实际测量数据抖动过大导致插值后差分明显不能反映实际情况，可以先对数据平滑（拟合或是平均）再求差分。(We can use least squares to solve general linear equat
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