svpwm3level

西蒙赫金———《自适应滤波器原理》第五章(failed to translate)

this is the code to illustrate greedy algorithm in matlab

说明：  很好的matlab实用程序，从光盘中拷出来的。(Matlab good utility, from the CD-ROM copy from.)

使用C++和matlab混合编程实现四叉树分解及合并区域的代码(C++ and matlab mixed programming quadtree decomposition and Mergers area code)

数值解的方法解释“战争使捕鱼量下降，食用鱼增加，鲨鱼等也随之增加，但鲨鱼的比例却大幅增加”这一现象。(Numerical interpretation of the " war to the decline of fishing, increased consumption of fish, sharks, also increased, but the proportion of sharks has increased substantially," the phenomenon.)

Pulse Modulated Signal Mfile

病态线性方程组的计算题，涉及Gauss消元法、雅可比迭代法、高斯-赛德尔迭代法、最速下降法和共轭梯度法。每一个方法，都编写一个m文件，封装成函数的形式。然后通过总的HilbLineEquSet.m文件来调用执行，画出误差曲线图，得到运行结果。总的Matlab程序流程，如下所示： 病态方程组的计算包括：HilbLineEquSet.m、gauss.m、jacobi.m、gauss_seidel.m、fastest_descend.m和conjugated_grad.m六个文件。 程序执行结果包括：求解结果、迭代次数、迭代误差数据、误差曲线图等。 (Morbid Linear Equations calculation problems involving Gauss elimination method, Jacobi' s iterative method, Gauss- Seidel iterative method, steepest descent method and conjugate gradient method. Each method, all the preparation of an m file, packaged in the form of a function. Then the total HilbLineEquSet.m file to invoke the implementation of draw error curve, to be running results. General Matlab program flow, as follows: the calculation of morbid equations include: HilbLineEquSet.m, gauss.m, jacobi.m, gauss_seidel.m, fastest_descend.m and conjugated_grad.m six documents. Procedures for implementation of the findings include: solving a result, the number of iterations, iterative error data, error curve and so on.)

已知一个LTI系统的差分方程为： y[n]-1.143*y[n-1]+0.4128*y[n-2]=0.0675*x[n]+0.1349*x[n-1]+0.0675*x[n-2] 初始条件y(-1)=1,y(-2)=2，输入x(n)=u(n)，计算系统的零输入响应 (known LTI system of a differential equation : y [n]-1.143* y [n-1] 0.4128* y [n-2] x = 0.0675* 0.1349 [n]* x [n-1] 0.0675* x [n-2] initial conditions y (1) = 1, y (2) = 2, input x (n) = u (n), we calculate the zero input response)

matlab program for papr reduction using companding technique

说明：  主成分分析法将图像分为低秩部分和稀疏部分(Principal Component Analysis (PCA) divides images into low rank and sparse parts)