hengxujing

牛顿迭代法用于求解非线性方程组，及求解范围(Newton iterative method for solving nonlinear equations, and solve the range)

选择一张灰度图像和一张彩色图像，经过matlab仿真后能够将彩色图像上的颜色转移到灰度图像上！！！(Select a gray image and a color image, through the matlab simulation, the color images can be transferred to the gray color of the image! ! !)

矩阵实例，有题目和相应的代码及仿真图像，方便初学者轻松认识matlab编程思想(Matrix instance, there are topics and the corresponding code and simulation images to facilitate easy understanding of matlab programming ideas for beginners)

用matlab进行AMI编码的调试并进行文本的输出与输入 同时还可以直观的看到波形图(MATLAB program used to prepare the AMI modulation and demodulation process, and in order to simulate the process of digital communication system, in the modulated Gaussian white noise after.)

基于压缩感知(应用匹配追踪算法)处理二维信号(MP2D compressed sensing using mp)

对X应用贝叶斯分类，对X的前两类训练单层感知器(The classification of X application of Bias，on the X of two kinds of training single-layer perceptron)

this is code for finding and tracking feature points

How to use the ordinary Voronoi partition generator. The m-files also have their own help documentation that you can view in Matlab. This code creates a bounding polygon that is a square. Then it randomly creates 10 points and then creates and draws the partition. bounds = [0, 10, 10, 0 0, 0, 10, 10] points = 10 * rand(2, 10) regions = voronoi(bounds, points) drawRegions(bounds, regions) ================================ This is a brief set of instructions on how to use the Multiplicatively- weighted Voronoi partition generator. Each of the m-files has a help section, so in Matlab you can call: help mwvoronoi help drawRegions Here is an example that creates a bounding polygon, a square with side length 10. Then it randomly creates six points with random weights. Finally, the code draws the resulting partition. bounds = [0, 10, 10, 0 0, 0, 10, 10] points = 10 * rand(3, 6) regions = mwvoronoi(bounds, points) drawRegions(bounds, regions) Enjoy!

truetime仿真机器人踢足球 matlab仿真(truetime soccer matlab)

说明：  seir模型模拟传染病 人在康复以后产生了抗体就不会再得病。不同于SIS模型，我们在模型中引入康复者(The Recovered)，用 [公式] 表示，并满足总人数 [公式] 。这个时候就是SIR模型。一旦变为康复者，就不会再传染，即在概率传递过程中，一旦变为康复者，就没有概率再次转移为感染者或者易感者。我们假设感染者变为康复者的概率为我们来看下SIR模型的微分方程(SEIR model simulating infectious diseases)