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load_flow
calculation of load flow in power system using matlab
power engineering field
- 2013-07-18 16:59:20下载
- 积分:1
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ofdm
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
- 2015-04-10 17:09:57下载
- 积分:1
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ebp4
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)
- 2009-04-28 00:50:18下载
- 积分:1
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whitenoiseandsth
to generate the white noise signal u(n) with uniform distribution and power p
- 2010-11-16 16:46:52下载
- 积分:1
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fourorderculumantsica
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)
- 2008-03-14 14:19:11下载
- 积分:1
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m
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)
- 2010-02-19 10:26:48下载
- 积分:1
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speechenhancement
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)
- 2013-07-09 15:22:08下载
- 积分:1
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baojing
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! ! !)
- 2013-12-09 21:34:18下载
- 积分:1
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Informax_FastICA.rar
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.)
- 2009-12-24 15:20:32下载
- 积分:1
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MyKmeans
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))
- 2005-07-26 01:32:58下载
- 积分:1