LU.tar

说明：  相位解缠和相位展开matlab程序，里面还包含多篇介绍该相关解缠算法的文章(Phase unwrapping and phase unwrapping matlab program, which also includes more than to introduce the relevant article unwrapping algorithm)

加汉宁窗fft插值算法，计算谐波幅值、频率、相位，需输入采样频率、fft长度(hanning windows Harmonic analysis)

此程序为潮流计算的最后一部分功率计算程序。(This program is the last part of the power flow calculation calculation program.)

说明：  同时求取不同信号的多个时域特征参数，可以随意更换待分析信号(Multiple time domain characteristic parameters of different signals are obtained at the same time.)

说明：  数值分析中运用matlab使用LU分解法求线性方程组(Numerical analysis using matlab LU decomposition method for solving linear equations)

说明：  能够实现线性回归,里面有一个线性回归的实例(It can realize linear regression. There is an example of linear regression)

用Lanczos方法求解矩阵的最大几个奇异值的Matlab程序。(Matlab codes for large singular value with Lanczos Methods)

fluent多相流DPM模型中使用用户自定义函数对颗粒进行统计(DPM fluent multiphase flow model using user-defined functions for particle statistics)

％ 奇异值分解 (sigular value decomposition,SVD) 是另一种正交矩阵分解法；SVD是最可靠的分解法， ％ 但是它比QR 分解法要花上近十倍的计算时间。[U,S,V]=svd(A)，其中U和V代表二个相互正交矩阵， ％ 而S代表一对角矩阵。 和QR分解法相同者， 原矩阵A不必为正方矩阵。 ％ 使用SVD分解法的用途是解最小平方误差法和数据压缩。用svd分解法解线性方程组，在Quke2中就用这个来计算图形信息，性能相当的好。在计算线性方程组时，一些不能分解的矩阵或者严重病态矩阵的线性方程都能很好的得到解( Singular value decomposition (sigular value decomposition, SVD) is another orthogonal matrix decomposition method SVD decomposition is the most reliable method, but it takes more than QR decomposition near ten times the computing time. [U, S, V] = svd (A), in which U and V on behalf of two mutually orthogonal matrix, and the S on behalf of a diagonal matrix. And QR decomposition are the same, the original matrix A is no need for the square matrix. The use of SVD decomposition method are used as a solution of least squares error method and data compression. Using SVD decomposition solution of linear equations, in Quke2 on to use this information to calculate the graphics performance quite good. In the calculation of linear equations, some indecomposable matrix or serious pathological matrix of linear equations can be a very good solution)

用matlab仿真，通过不同的方法计算矩阵的SVD分解，并用SVD的方法计算最小二乘问题和进行图像压缩(Matlab simulation, different ways to calculate the matrix SVD decomposition and the SVD method to calculate the least squares problem and the image compression)