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nnrbf_pid
在MATLAB中使用，S函数现实RBF-PID控制程序。
(S function of the reality on RBF-PID control)
- 2012-04-23 10:01:23下载
- 积分：1
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correlation
correlation programs in matlab
correlation programs in matlab
correlation programs in matlab
- 2013-09-28 18:53:27下载
- 积分：1
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material-dispersion
用来求光纤的材料色散，并画出其材料色散曲线。(material dispersion)
- 2014-12-20 11:10:30下载
- 积分：1
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NpIca-v1.2
基于非参数概率密度估计的盲源分离算法（NpICA），使用matlab编程，有可视界面。(Non-parametric probability density estimation-based blind source separation algorithm (NpICA), using the Matlab programming, visual interface.)
- 2013-04-23 10:19:14下载
- 积分：1
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TDD_mode
3gpp release5中对TDD模式的详细描述(3gpp release5 TDD mode to a detailed description of)
- 2007-04-04 21:05:04下载
- 积分：1
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Simple_adpative
it involves the concept of a simple adaptive filter
- 2010-12-08 18:37:29下载
- 积分：1
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Emgu-CV-Tutorial
a document about Emgu CV learning.
good luck
- 2014-09-20 18:39:36下载
- 积分：1
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rake
用matlab实现了CDMA系统的RAke接收机，比较最大比合并，等增益合并和选择式合并接收机的性能(Using matlab to achieve a CDMA system RAke receiver, compare maximal ratio combining, equal gain combining and selective merge receiver performance)
- 2013-09-03 22:08:21下载
- 积分：1
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APjulei
说明： 可以实现对Excel中数据的读取和聚类，并输出聚类结果(It can read and cluster data in Excel and output clustering results.)
- 2019-05-29 14:57:13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
