Automotive-brake-curve

实现聚类K均值算法： K均值算法：给定类的个数K，将n个对象分到K个类中去，使得类内对象之间的相似性最大，而类之间的相似性最小。 缺点：产生类的大小相差不会很大，对于脏数据很敏感。 改进的算法：k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用，这样的一个medoid就标识了这个类。步骤： 1，任意选取K个对象作为medoids（O1,O2,…Oi…Ok）。 以下是循环的： 2，将余下的对象分到各个类中去（根据与medoid最相近的原则）； 3，对于每个类（Oi）中，顺序选取一个Or，计算用Or代替Oi后的消耗—E（Or）。选择E最小的那个Or来代替Oi。这样K个medoids就改变了，下面就再转到2。 4，这样循环直到K个medoids固定下来。 这种算法对于脏数据和异常数据不敏感，但计算量显然要比K均值要大，一般只适合小数据量。(achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n will be assigned to target K to 000 category, making target category of the similarity between the largest category of the similarity between the smallest. Disadvantages : class size have no great difference for dirty data is very sensitive. Improved algorithms : k-medoids methods. Here a selection of objects called mediod to replace the center of the above, the logo on a medoid this category. Steps : 1, arbitrary selection of objects as K medoids (O1, O2, Ok ... ... Oi). Following is a cycle : 2, the remaining targets assigned to each category (in accordance with the closest medoid principle); 3, for each category (Oi), the order of selection of a Or, calculated Oi Or replace the consumption-E (Or))

说明：  网格映像法仿真瑞利高斯分布，单个辐射单元，单个距离单元，杂波面(Simulation of Rayleigh imaging method grid Gaussian distribution, a single radiating element, a single range cell, surface clutter)

MATLAB中simulink环境下S函数编写的教程(MATLAB simulink environment in the S function written tutorial)

lte中CQI的编码代码,在标准211中可以看到原理(the cQI R-M coding in Pucch 2)

雷达常规单载频信号和LFM信号的MATLAB仿真(MATLAB simulation of single carrier frequency signal)

Matlab code that has an exercise about signals processing.

MATLAB平台运行的禁忌搜索算法代码，亲测可用(Tabu search algorithm code running on MATLAB platform, pro test available)

source code in Matlab, function for image thinning, using 2 different algorithm for image thinning. So you can compare that.

matlab下的神经网络感知机实验代码，提供给有需要的人(matlab neural network under the experimental code Perceptron)

matlab在线性规划中函数的简单应用。(matlab online planning applications in a simple function)