four-filter

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：  MatLab在工程数学方面的应用,工程数学计算.(MatLab in the application of engineering mathematics, engineering, mathematical calculations.)

用逆累积分函数算出随机变量X满足拉普拉斯分布的随机数 (Random variable X is calculated using the inverse tired integral function to meet the Laplace distribution of the random number)

the third generation PP

说明：  Identification of motor guide using matlab

用LS法估计的AR参数并用Cadzow谱估计子估计出信号的功率谱密度(With the LS estimation of the AR parameters and spectral estimation Cadzow child with estimated signal power spectral density)

卡尔曼滤波算法和扩展卡尔曼滤波算法在目标跟踪到的应用(Kalman filter and extended Kalman filter algorithm to the application in target tracking)

dea in matlab for windows

基于MATLAB环境下的数字信号的调制（四种调制方式的具体实现方法）(Based on digital signal modulation MATLAB environment (the concrete realization of the four modulation method))

遗传算法优化PID，解决传统的PID参数采用试凑的方式的问题，从而进行自整定参数( Genetic Algorithm PID，Solve using traditional PID parameters of trial and error approach problems in order to be self-tuning parameters)