NetonLiterator

高光谱解混合方法，变分增强拉格朗日线性解混合方法。(A Variable splitting Augmented Lagragian Approach to Linear Spectral Unmixing)

近似熵和互近似熵的matlab代码，用于脑波分析很有用(Approximate entropy and cross approximate entropy matlab code for brain wave analysis useful)

％ 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)

外国人写的卡尔曼滤波器算法的库，可以供大家参考，已经封装好了(kalman library)

说明：  Godunov格式和Roe格式求解Burgers方程(Godunov scheme and Roe scheme for solving Burgers equation)

这个程序是关于轴承刚度方程的MATLAB程序(This program is about the bearing stiffness equation MATLAB program)

An example of using a matrix object in order to pass values and complete a function.

自己编的5种调制信号，通过反复训练模板能有较高的识别率，DC-DC部分采用定功率单环控制。( Own five modulation signal, Through repeated training VxODOGFlate have higher recognition rate, DC-DC power single-part set-loop control.)

说明：  龙格-库塔方法的使用，例子 求y’=y-2x/y，y(0)=0。 解：输入X0=0，X1=1，H=0.2，N=6。()

基于matlab的约束非线性规划算法库，适于学习！(Matlab-based algorithm for the constrained nonlinear programming library, suitable for learning!)