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Airfoil_Analyzer
Matlab编写的一个翼型分析软件，且包含了大量常见的翼型数据(An airfoil analysis software written in Matlab, and contains a large number of common airfoil data)
- 2009-10-31 10:03:46下载
- 积分：1
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optimum
优化程序 基本优化算法 使用的理论是遗传算法(Optimizer basic genetic algorithm optimization)
- 2009-12-28 18:56:40下载
- 积分：1
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BER_QPSK
Bit error probability for BPSK, MATLAB code
- 2011-07-16 11:38:21下载
- 积分：1
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Markov-Chains-examples-with-codes
马尔科夫链的matlab仿真,马尔可夫模型（Markov Model，MM）是统计模型，它用来描述一个含有隐含未知参数的马尔可夫过程。(Matlab simulation of Markov chains, Markov models (Markov Model, MM) is a statistical model, which is used to describe an unknown parameter containing hidden Markov process.)
- 2013-07-25 07:29:31下载
- 积分：1
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plotspec
Plays piano sound in matlab
- 2015-03-07 23:10:24下载
- 积分：1
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houjie_v41
这是一个好用的频偏估计算法的matlab仿真程序,LDPC码的完整的编译码，FIR 底通和带通滤波器和IIR 底通和带通滤波器。( This is a useful frequency estimation algorithm matlab simulation program, Complete codec LDPC code, Bottom-pass and band-pass FIR and IIR filter bottom pass and band-pass filter.)
- 2016-06-02 21:37:35下载
- 积分：1
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improved-leach
this is a source code about leach
- 2013-02-11 14:26:58下载
- 积分：1
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juli
三维重构计算空间距离，在图像匹配后计算得到。(Three-dimensional reconstruction calculated distance, the images calculated after matching.)
- 2010-09-09 14:36:40下载
- 积分：1
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chazhinihe
matlab的经典算法--用于各种插值与拟合(matlab classical algorithm- interpolation and fitting)
- 2010-01-29 10:24:45下载
- 积分：1
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NewK-means-clustering-algorithm
说明： 珍藏版，可实现，新K均值聚类算法，分为如下几个步骤：
一、初始化聚类中心
1、根据具体问题，凭经验从样本集中选出C个比较合适的样本作为初始聚类中心。
2、用前C个样本作为初始聚类中心。
3、将全部样本随机地分成C类，计算每类的样本均值，将样本均值作为初始聚类中心。
二、初始聚类
1、按就近原则将样本归入各聚类中心所代表的类中。
2、取一样本，将其归入与其最近的聚类中心的那一类中，重新计算样本均值，更新聚类中心。然后取下一样本，重复操作，直至所有样本归入相应类中。
三、判断聚类是否合理
采用误差平方和准则函数判断聚类是否合理，不合理则修改分类。循环进行判断、修改直至达到算法终止条件。(NewK-means clustering algorithm ，Divided into the following several steps:
A, initialize clustering center
1, according to the specific problems, from samples with experience selected C a more appropriate focus the sample as the initial clustering center.
2, with former C a sample as the initial clustering center.
3, will all samples randomly divided into C, calculate the sample mean, each the sample mean as the initial clustering center.
Second, initial clustering
1, according to the sample into the nearest principle clustering center represents the class.
2, as this, take the its recent as clustering center of that category, recount the sample mean, update clustering center. And then taking off, as this, repeated operation until all samples into the corresponding class.
Three, judge clustering is reasonable
Adopt error squares principles function cluster analysis.after clustering whether reasonable, no reasonable criterion revisio)
- 2011-04-06 20:45:56下载
- 积分：1
