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logicaloperation
logical operation in matlab for image manipulationg
- 2010-07-23 23:35:14下载
- 积分：1
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interpolation-examples
Interpolation refers to the process of converting a sampled digital signal (such as a sampled audio signal) to a higher sampling rate (Upsampling)
- 2012-05-04 04:33:52下载
- 积分：1
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Matlab-GUI-tuxiangchuli
利用MATLAB中的GUI（图形用户界面），实现图像的读取，边缘检测，四叉树分解，直方图阈值分割，二值化差值的实现，并设计了退出按钮。(In MATLAB GUI (graphical user interface), to achieve image reading, edge detection, quad-tree decomposition, histogram thresholding, binary difference implementations and designed the Exit button.)
- 2014-11-13 13:34:14下载
- 积分：1
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Complete
a doctorate thesis for dynamic multi-objective optimization
- 2015-01-18 16:44:08下载
- 积分：1
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CG
说明： 利用共轭梯度法求线性方程组的Matlab算例(Using conjugate gradient method for solving linear equations Matlab examples)
- 2015-03-31 11:05:02下载
- 积分：1
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matlab100program
这是一个100个MATLAB 实例程序，也是从网上搜索到的，对大家有用才好。(This is a 100 MATLAB Examples of procedures, but also from the Internet to search for useful everyone to do.)
- 2007-07-26 11:08:14下载
- 积分：1
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K-meanCluster
How the K-mean Cluster work
Step 1. Begin with a decision the value of k = number of clusters
Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following:
Take the first k training sample as single-element clusters
Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster.
Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample.
Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)
- 2007-11-15 01:49:03下载
- 积分：1
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untitled6
trasform program good
- 2011-01-09 13:57:52下载
- 积分：1
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ColorDetectionByHue
implementing color detection by using the hue technique
- 2011-01-12 02:39:02下载
- 积分：1
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DGPSO
差分进化算法代码,请下载使用，很有学习价值，我已验证可以使用(Differential evolution algorithm code, please download, it is worth learning, I' ve verified that you can use)
- 2013-11-17 20:54:26下载
- 积分：1
