Shengjin-Program-Original

计算各种气象物理量，包括散度、涡度、螺旋度、垂直速度、水汽通量、比湿、假相当位温、k指数等的grads 程序，(Calculate various meteorological physical quantity, including divergence, vorticity, helicity, vertical velocity, water vapor flux, specific humidity, pseudo-equivalent temperature, k index grads program,)

gui of music algorithm implemented in matlab

曲线拟合的最小二乘法要解决的问题，实际上就是求以下超定方程组的最小二乘解的问题。(Least squares curve fitting to solve the problem, in fact, find the following overdetermined least squares solution for the problem group.)

MATLAB编写的非结构化网格生成程序，不算很长(MATLAB program written in unstructured mesh generation, not very long)

包括最小二乘法、SVM、神经网络、1_k近邻法,合成孔径雷达（SAR)目标成像仿真，主要是基于mtlab的程序。( Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Synthetic Aperture Radar (SAR) imaging simulation target, Mainly based on the mtlab procedures.)

matlab计算的自适应阈值分隔，非常好用，适用于初学者(matlab adaptive threshold calculation separated, very useful for beginners)

随机生成裂缝网格，由于地下水文学模拟，及裂缝网格连通性计算。(Randomly generated grid of cracks, due to subsurface hydrology simulation and calculation of cracks grid connectivity.)

分别采用Roe格式和AUSM格式求解一维黎曼问题，并将计算结果与解析解进行比较，发现这两种计算格式都能获得高的计算精度。(Roe scheme were used in this procedure and AUSM format to solve a 1-D RIEMANN problem, and numerical results were compared with the analytical solution, the results can be seen from these two formats have a solution with high accuracy.)

贝叶斯网络结构学习爬山算法的改进方法，更准确(Improved Bayesian network structure learning climbing algorithm, more accurate)

PCA算法程序设计步骤： 1、去均值 2、计算协方差矩阵及其特征值和特征向量 3、计算协方差矩阵的特征值大于阈值的个数 4、降序排列特征值 5、去掉较小的特征值 6、去掉较大的特征值(一般没有这一步） 7、合并选择的特征值 8、选择相应的特征值和特征向量 9、计算白化矩阵 10、提取主分量 (PCA algorithm programming steps: 1 to mean 2, calculation of covariance matrix and its eigenvalues and eigenvectors 3, the calculation of covariance matrix eigenvalues greater than the threshold number of 4, in descending order of eigenvalue 5, remove the smaller eigenvalue 6, remove the larger eigenvalue (usually not this step) 7, the combined choice of eigenvalues 8, select the appropriate eigenvalues and eigenvectors 9, computing whitening matrix 10, principal component extraction)