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two_pole_PWM
two pole voltage switched PWM voltage control circuit in matlab&simulink
- 2009-04-30 21:20:42下载
- 积分：1
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functionFDTD
这是FDTDF电磁场一维数值算法的MATLABLE 程序。(FDTDF This is an electromagnetic field-dimensional numerical algorithm MATLABLE procedures.)
- 2006-09-24 14:10:57下载
- 积分：1
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Practical.Programming.In.Tcl.And.Tk.ed4.Prentice
Practical.Programming.In.Tcl.And.Tk,Tcl/Tk脚本语言最经典的教程,此为第四版(Practical.Programming.In.Tcl.And.Tk. Tcl/Tk scripting language classic handbook, which is the fourth edition)
- 2007-03-21 23:48:23下载
- 积分：1
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problem_1
Monte Carlo Simulation On Estimating the probability of a flush
- 2014-10-29 15:27:05下载
- 积分：1
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8-1
智能控制（刘金琨版）第8章第1题课后练习MATLAB程序(Intelligent Control (Hangzhou Press Edition) Chapter 8 Question 1 Homework MATLAB program)
- 2015-04-10 09:54:00下载
- 积分：1
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mssnr
we investigate a new blind adaptive fractionally-spaced TEQ (FS-TEQ) for multicarrier systems in order to reduce efficiently intersymbol interference. We also exploit the cyclic prefix and fractionally-spaced TEQ to create a blind adaptive globally convergent channel-shortening algorithm.
- 2009-10-21 15:45:41下载
- 积分：1
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Monte-Carlo-Simulation
蒙特卡洛模拟的程序，可以进行金融、经济、管理方面的研究之用(Example Monte Carlo Simulation in Matlab)
- 2011-04-21 23:44:08下载
- 积分：1
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5
说明： 信号处理,进行信号检测与处理，通过设定threshold来进行信号检测的可信度调整(Credibility adjustment signal processing, signal detection and processing, by setting threshold for signal detection)
- 2013-12-02 04:20:06下载
- 积分：1
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strong-tracking-filter
清华大学周东华教授提出的强跟踪滤波器，有效改善突发干扰下的跟踪能力。(Strong tracking filter supposed by Pro. ZHOU Dong-hua, which enhanced the tracking ability during sudden disturb.)
- 2020-12-11 11:39:19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
