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dc-motor
simulation du moteur a courant continu
- 2011-12-06 03:18:09下载
- 积分：1
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ML-gradient
ML gradient descend ，Stanford course(machine learning ，Andrew Ng)
- 2013-11-16 08:37:11下载
- 积分：1
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codes
some soucre code for clustering
- 2010-07-05 16:05:14下载
- 积分：1
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fastvalue
一种基于图像像素分类的快速计算图像清晰度评价值函数。(Pixel classification based on image sharpness of the image evaluation value fast computing functions.)
- 2011-01-14 16:49:05下载
- 积分：1
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BTT6DOF
btt源码 六自由度仿真，全弹道仿真，倾斜转弯(btt source 6-DOF simulation)
- 2012-05-11 09:03:02下载
- 积分：1
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matlab-optimization-algorithms
Matlab 最优化算法程序以及算例
包含各类最优化求解算法的m文件(matlab optimization algorithms
programs and examples)
- 2015-03-18 21:48:35下载
- 积分：1
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Slow-Feature-Analysis
Slow feature analysis using matlab
- 2013-09-27 18:00:06下载
- 积分：1
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luijun_v43
相参脉冲串复调制信号，实现串口的数据采集，线性调频脉冲压缩的Matlab程序。( Complex modulation coherent pulse train signal, Achieve serial data acquisition, LFM pulse compression of the Matlab program.)
- 2016-05-24 19:51:06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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matlabwaveanasys
说明： MATLAB 频谱分析的经典仿真，详细的源程序及仿真文件 (MATLAB spectral analysis of the classical simulation of the detailed source code and simulation files)
- 2010-03-18 09:44:38下载
- 积分：1
