ImmKalman

本程序是根据jacobi过关法求实对称矩阵的全部特征值和特征向量(This procedure is based on realistic method jacobi clearance of all symmetric matrix eigenvalue and eigenvectors)

说明：  模糊指数平滑算法源程序,进行预测 计算.(fuzzy exponential smoothing algorithm source for projection.)

弹性波数值模拟，可以修改模型，可以做波场模拟，做面波的地震记录((Numerical simulation of seismic surface waves, you can modify the model, you can do the wave field snapshots do surface wave seismograms))

实现了bch编码，基于有限域多项式除法,自己动手写的，大家可以看下(Based on division in galois field，relize the BCH code )

一些很有用的数值计算程序：包括多种数值逼近算法的C语言程序，如龙格逼近算法，里米兹一致逼近算法等(Some very useful numerical calculation procedures: including a variety of numerical approximation algorithm for the C language program, such as the Runge approximation algorithm, where meads uniform approximation algorithm)

理想流体势流的有限元求解程序,简单易懂，适合初学者对该方法编程的一个初步认识,程序内有详细的注释(Potential flow of ideal fluid finite element solver, easy to understand, this method is suitable for beginners programming a preliminary understanding of the program with detailed notes)

实现用fluent udf自定义k~e方程(Define the k~e equation by using fluent udf function.)

一些关于数学思想应用的matlab代码，以供大家学习分享；(The application of mathematical ideas about matlab code for them to learn to share )

％ 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)

说明：  克里金、反距离插值算法_栅格图等值线填充等(Kriging, inverse distance interpolation algorithm _ isogram filled grid map, etc.)