stance_dff

包括脚本文件和函数文件形式，MinkowskiMethod算法 ,抑制载波型差分相位调制。( Including script files and function files in the form, MinkowskiMethod algorithm, Suppressed carrier type differential phase modulation.)

可进行最小二乘法线性拟合输出，包含源代码及生成后的程序(Least-squares linear fit can be output)

说明：  二维FDTD程序计算近场模型，包括建模文件以及电磁场递推文件，入射角度为0°。模型时一个介质方柱。(The two-dimensional FDTD program calculates near-field models, including modeling files and electromagnetic field recursion files, with an incident angle of 0. The model is a medium square column.)

LS_SVM 偏最小二乘结合支持向量机(The LS SVM partial least squares and Support Vector Machine)

用蒙特卡洛模拟的方法计算美式期权的价格以及基本描述,能量熵的计算，sar图像去噪的几种新的方法。( Monte Carlo simulation method of calculating the American option price and basic description, Energy entropy calculation, Several new methods sar image denoising.)

迭代自组织数据分析，基于matlab GUI界面设计,有PMUSIC 校正前和校正后的比较。( Iterative self-organizing data analysis, Based on matlab GUI interface design, A relatively before correction and after correction PMUSIC.)

利用ARIMA处理分析时间序列，计算数列相关性和数字特征并通过计算所得值进行一定范围的预测(ARIMA processing analysis time sequence, to calculate a number of columns and digital characterized by the calculated value for a certain range of the prediction)

％ 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)

对比卡尔曼滤波和维纳滤波对一阶gaussian-markov过程的滤波预测。(Contrast Kalman filter and Wiener filter of first-order gaussian-markov forecast filtering process.)

实现矿井采空区的孔隙率和粘性阻力系数，主要用于采空区瓦斯抽采(The porosity and viscosity resistance coefficient of mine goaf can be achieved.)