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VCMatlab
VC++与MATLAB混合编程学习的很好电子书(VC++ and MATLAB programming mix of good books to learn)
- 2010-06-25 12:37:38下载
- 积分：1
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PCA_neural_networks
PCA for image compression, Sanger s algorithm implemented with neural networks
- 2011-01-31 22:19:13下载
- 积分：1
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RBF
神经网络，RBF聚类法和RBF自组织法的函数逼近的实现(Neural network, RBF clustering method and self-organizing RBF function approximation method to achieve)
- 2014-11-04 22:09:31下载
- 积分：1
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LocalService
internally keep track of Call states to maintain information for Call Waiting and 3Way for CDMA instance of Phone App.
- 2013-12-02 15:04:25下载
- 积分：1
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MatlabGUI
matlab的gui教程，非常详细，简单易懂，便于学习，非常适合新手入门(The gui matlab tutorial, very detailed, easy to understand, easy to learn, very suitable for beginners)
- 2013-11-14 20:32:18下载
- 积分：1
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print
Matlab classic program about Print
- 2013-12-28 03:57:30下载
- 积分：1
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matlab代码，实现神经网络中的BP算法-误差反向传播算法
说明： matlab代码，实现神经网络中的BP算法-误差反向传播算法，用于分类，拟合和非线性系统建模等多种问题(An example of BP neural network)
- 2020-04-05 17:02:29下载
- 积分：1
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dasushu
rsa 大素数生成算法和说明 基于MATlAb(rsa large prime number generation algorithm and instructions Based on Matlab)
- 2012-05-12 00:38:39下载
- 积分：1
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RLS
RLS算法去除加噪声音乐程序，效果比较好，有图为证。(RLS algorithm to remove noise adding music program, the effect is better, has a license to the graph.
)
- 2013-12-18 10:20:11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
