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rayleigh
瑞利信道的仿真。结果为信号时域的图。仿真衰落的过程。(Rayleigh channel simulation. The results of time-domain signal of Fig. Simulation of the process of decline.)
- 2007-12-13 12:55:44下载
- 积分：1
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rungekutta
runge kutta方法求解常微分方程(the Runge–Kutta methods (German pronunciation: are an important family of implicit and explicit iterative methods for the approximation of solutions of ordinary differential equations. These techniques were developed around 1900 by the German mathematicians C. Runge and M.W. Kutta.)
- 2012-04-24 17:10:20下载
- 积分：1
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similition
this group file are similition by matlab
- 2014-12-15 04:43:57下载
- 积分：1
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m
说明： 数字通信中，对面对判决环和非面对判决环的相位估计的仿真，调制方式采用QPAK调制方式。(Digital communication for non-face to face judgment ring and phase estimation of judgment ring simulation, modulation using QPAK modulation.)
- 2014-12-25 10:46:29下载
- 积分：1
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Matlab数学建模经典案例实战源程序
matlab数学建模，matlab程序，实例代码皆有。包含着许多的建模算法程序实例(MATLAB mathematic modeling, matlab program)
- 2017-09-30 14:54:31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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Imagefilterin
filteirng that can be done where ever possible with compression
- 2011-02-03 05:38:46下载
- 积分：1
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Matlab_BP
说明： Matlab的神经网络工具箱实用指南.doc(Matlab neural network toolbox Practical Guide. Doc)
- 2006-04-01 16:20:30下载
- 积分：1
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tsp-zafarian
tsp solution by amin zafarian
- 2011-06-12 02:51:11下载
- 积分：1
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Mvdr_Original
相控阵雷达处理MVDR 自适应算法 没有考虑宽带问题(MVDR adaptive algorithm)
- 2012-09-06 23:33:21下载
- 积分：1
