chepaishibie

ofdm的.m文件,是ofdm仿真的m文件(ofdm of. m document file ofdm simulation m)

基于传递矩阵的方法，计算转子动力学系统临界转速及振型(Critical Speed and Vibration Mode of Rotor Dynamics System Based on Transfer Matrix Method)

newmark求解线性结构动力反应，非线性不行，可用于有限元(Newmark solving dynamic responses of linear structures)

C/C++中调用MATLAB引擎的数值仿真的实现， 微分方程文件“fun.m”放在matlab的work目录下(C/C++ in the MATLAB engine called the realization of the numerical simulation, differential equations documents " fun.m" on the work directory matlab)

matlab_几乎涵盖了所有命令，很全，很实用！(matlab_ cover almost all the orders, very wide, very practical!)

各式各样的扩散为基础的图像滤波方法: 1。线性扩散滤波使用热方程,解决了使用隐式和显式欧拉方法。 2。边缘增强线性各向异性扩散滤波。 3。边缘增强非线性各向异性扩散滤波。(The spread of a variety of image filtering method based on: 1. Linear diffusion filter using the heat equation, solved using implicit and explicit euler method. 2. Edge enhancement of linear anisotropic diffusion filter. 3. Edge enhancement of nonlinear anisotropic diffusion filter. )

基于简单一元函数的遗传算法，通过使用Shdffield工具箱，更好的实现了遗传算法的每一步，并将其结果以图的形式展现出来。代码清晰易懂。(Functions of one variable based on a simple genetic algorithm, by using Shdffield toolbox to achieve better every step of the genetic algorithm, and the results show up in the form of graphs. Code is clear and understandable.)

1.掌握图像滤波的基本定义及目的； 2.理解空域滤波的基本原理及方法； 3.掌握进行图像的空域滤波的方法。 (1. Image filtering grasp the basic definition and purpose; 2. Understanding of spatial filter the basic principles and methods; 3. master images of the spatial filter method.)

一种matlab环境下开关磁阻电机的仿真研究(Under a matlab simulation of switched reluctance motor)

In computer science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle s position and velocity. Each particle s movement is influenced by its local best known position but, is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions.