buitei

同伦算法解非线性方程组，构造不动点问题,并从不同的初始点出发运行程序。(To solve nonlinear equations with homotopy algorithm，constructing a fixed foint progrem.and from the different starting point to run the program. )

非常实用的时域仿真法暂态分析工具，利用牛顿法求解系统的微分方程组和代数方程组，得到转子摇摆曲线，判断暂态是否稳定(Very useful time-domain simulation tool for transient analysis method, using Newton method for solving systems of differential equations and algebraic equations, the rotor swing curves, to determine the stability of transient)

圆周卷积滤波器，可清晰见到实际滤波器的算法(Circular convolution filter, can clearly see that the actual filter algorithm)

使用C编写的复数矩阵求逆，使用高斯消去法，已经和matlab结果做过对比，无误(Written in C and the complex matrix inverse, using the Gaussian elimination method, has been done and matlab results contrast, correct)

高震荡积分程序，可以计算以cos函数为积分核的高震荡积分(High shock points program that can calculate the cos function as integral kernel of the high-shock points)

膜渗透 FUENT UDF 气液两相流 (Gas-liquid two-phase flow membrane permeation FUENT UDF)

说明：  基于AOA的室外雷达定位算法仿真，基于TDOA的室外雷达定位算法仿真，基于AOA doa的室外雷达定位算法仿真，三维(Simulation of outdoor radar location algorithm based on AOA, simulation of outdoor radar location algorithm based on TDOA, simulation of outdoor radar location algorithm based on AOA TDOA, 3d)

C-N解偏微分方程的程序。解的是定步长的抛物型偏微分方程： du/dx - a * d2u/dx2 = 0 在程序中可以更改 a 的值以实现不同系数的解。在循环中改变 a 的值以实现变系数. 该说明在压缩包是也有(CN solution of partial differential equations procedures. Solution is scheduled to step parabolic partial differential equation: du/dx- a* d2u/dx2 = 0 in the procedure can be changed in order to realize the value of a different coefficient of the solution. Change in the cycle in order to realize the value of a variable coefficient. The note in the compressed packet is also)

％ 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)

Training a 3 layers feed forward neural network using back propagation algorithm