sfrmat2_guide

calculation of load flow in power system using matlab power engineering field

OFDM channel Using a large number of parallel narrow-band subcarriers instead of a single wide-band carrier to transport information l Advantages » Very easy and efficient in dealing with multi-path » Robust again narrow-band interference

BP神经网络例子，主要用于图像处理以及数据挖掘(Examples of BP neural network, mainly for image processing and data mining)

to generate the white noise signal u(n) with uniform distribution and power p

独立分量分析的累积量方法，分离信号 ica方法(Independent Component Analysis cumulant method, separation method of signal ica)

说明：  m序列Matlab程序，一个主程序，一个调用子程序(m sequence of Matlab program, a main program, a subroutine call)

在MATLAB环境下使用谱减法对语音信号进行增强，程序可以运行(In the MATLAB environment using spectral subtraction speech signal enhancement program can run)

matlab编写的视频监控采集和目标提示软件，请批评指正！！！(matlab prepared video surveillance and target acquisition software tips, please criticism! ! !)

一个基于信息最大化的盲信号分离算法，详细的文章可以在网络上搜索。(One based on information maximization algorithm for blind signal separation, detailed article on the network search.)

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))