KNN

说明：  C-means一个简单完整的程序，供大家看一下，有不完善的地方，请多多指教(C-means a simple and complete procedures for Members to see, any problems, please exhibitions)

fuzzy-neural simulation

跳频系统的Simulink模块实现,运行很快很好用(The Simulink module of frequency hopping systems, running good soon)

《MATLAB在控制系统中的应用》实例程序，随书光盘(MATLAB application in control system" program, CD with the book)

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))

this file is simulink of self inverter in matlab software

混合群智能优化的模糊PID控制,该设计用于汽轮机调速器的设计(A Hybrid PSO_Fuzzy_PID Controller for Gas Turbine Speed Control)

经验模态分解函数，可实现信号分解及滤波的功能。(EMD scheme which can achieve signal decomposition and noise elimanition.)

Fast Fourier Transform

DOTNET- Finger Print Authentication Using Biometric Systems