xianshi

idl环境下运行，计算两幅图像的交叉相关系数，用于变化检测方面的研究(idl environment to run the calculation two images of cross-correlation coefficient for change detection studies)

crackprop_modeI_lefm_xfem_cpe4，ABAQUS基于线弹性断裂力学的扩展有限元法实例。(crackprop_modeI_lefm_xfem_cpe4 ABAQUS based on linear elastic fracture mechanics extended finite element method instance.)

不可压缩后稳态径向滑动轴承润滑计算方法，等刚度，不考虑负压(Incompressible steady-state radial sliding bearing lubrication calculation method on the stiffness, do not consider the negative pressure)

用于图像处理的独立分量分析，分形维数计算的毯子算法matlab代码,有井曲线作为输入可计算其地震波的衰减。( Independent component analysis for image processing, Fractal dimension calculation algorithm matlab code blankets, There is a well attenuation curve as input to calculate its seismic waves.)

非线性光学书后程序，分布求解，参考作用研究问题不同，方程不同(Nonlinear Optics Post-book Procedure)

高斯回归建立代理模型行协方差函数的选着的变成(gpml-matlab-v3.1-2010-09-27)

使用经典R/S方法计算赫斯特指数，以衡量数据的分型特性(using R/S method to caculate the Hurst exponent)

对于方程组AX=b，当系数矩阵非奇异时，对原方程进行改写，利用公式X^k=BX^(k+1)+g进行迭代求解。(For equations AX = b, when the coefficient matrix is ​ ​ nonsingular, rewriting the original equation, using the formula X ^ k = BX ^ (k+1)+g for iterative solution.)

％ 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)

灰色预测模型，对未来数据进行预测，同时还有误差检测(GM(1,1),Grey prediction model, which can forecast future data and detect errors at the same time)