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2
说明： 非最小相位MA（3）,高阶统计分析，GM算法估计MA参数，Matlab源程序(GM algorithm estimates MA parameters)
- 2009-05-30 15:13:06下载
- 积分：1
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totalaf
It is a simple matlab code for array factor.
- 2011-05-15 09:19:06下载
- 积分：1
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Seminario-Matlab
matlab par velocidad motores cd shunt serie compuesto
- 2014-01-08 04:02:22下载
- 积分：1
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xiaoqiupengzhuang
本程序主要模拟小球与边界碰撞，运用能量衰减的运动规律进行程序设定(This procedure simulated ball and border collision, the use of the energy decay law of motion for the program set)
- 2011-11-21 09:28:58下载
- 积分：1
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Array-signal-processing-
阵列信号处理方面的10个经典程序,主要为DOA估计(Array signal processing 10 classic program)
- 2013-11-13 11:21:13下载
- 积分：1
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ClusterPackV10
聚类分析工具箱 亚历山大博士写的，用于聚类分析，功能比较全(Cluster Analysis and Cluster Ensemble Software
ClusterPack is a collection of Matlab functions for cluster analysis. It consists of the three modules ClusterVisual, ClusterBasics, and ClusterEnsemble as described in the following. They are a selection out of my personal codebase for machine learning research. They contain general clustering algorithms as well as special algorithms developed in my research as indicated in the README files)
- 2009-04-11 15:46:17下载
- 积分：1
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1234453238790
离散数据点三维数据的图相模拟m代码，通用版本(three-demension drawing in the matlab)
- 2010-11-26 21:38:44下载
- 积分：1
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002
关于MATLAB 的学习资料 该资料通俗易懂 对初学者很有参考的价值(MATLAB-learning materials on the information easy to understand the value of a good reference for beginners)
- 2011-08-05 15:56:46下载
- 积分：1
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blbpfenkuaixianshi
这个主要是人脸识别的分块显示程序，是三阶临进的(This is the main face recognition sub-block display program, and third-order Pro into)
- 2012-05-27 09:38:09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
