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SVM
支持向量机的MATLAB实现，能自动完成数据的分类功能。(MATLAB support vector machines to achieve, can auto-complete the classification of functional data.)
- 2009-07-03 19:03:01下载
- 积分：1
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zhengqihuanxiang
蒸气幻想简单挂机脚本.jar
供研究Script的玩家学习(steam fantasy simple hook script. Script jar for research study of the player. .)
- 2006-12-03 23:36:38下载
- 积分：1
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SVM
support vector machine （支持向量机）方法是目前分类方法中比较好的一个分类方法，实验证明准确度非常高！(support vector machine (support vector machine) method is a better classification of a classification method, experimental proof of a very high accuracy!)
- 2008-07-19 17:54:23下载
- 积分：1
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linearandnearest
图像插值算法（最近邻域和双线性），很好用。(image interpolation algorithm (recent neighborhood and bilinear), is useful.)
- 2007-05-20 11:12:28下载
- 积分：1
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Simulinkdedianlidianzhifangzhen
关于MATLAB的电力电子仿真，自己收藏吧，值得收藏(About the MATLAB simulation of power electronics, his collection of it, worthy of collection)
- 2014-11-30 21:53:19下载
- 积分：1
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miaobiao
GUI实现秒表， ，有开始暂停结束功能(GUI stopwatch)
- 2013-11-26 20:36:52下载
- 积分：1
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codebuilder_awv6aq
通信原理的作业，利用matlab产生两路模拟语音信号，经过jhpRhk编码、时分复用、NHoNTuW调制经过同一个信道单向传输到对应的接收端。仿真了端到端通信的链路质量和丢包率情况，测试完整可用。.
(Communication Theory of operations, the use matlab to generate two analog voice signals encoded through jhpRhk , time division multiplexing, NHoNTuW modulation over the same one-way transmission channel corresponding to the receiving end. Simulation of the link quality and packet loss rate case end communications, testing complete available.
)
- 2016-01-01 21:02:54下载
- 积分：1
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PCA-analysis
利用Matlab编程实现主成分分析,从数学角度来看，这是一种降维处理技术(Using Matlab programming principal component analysis, from a mathematical point of view, this is a dimension reduction process technology)
- 2011-10-04 16:50:08下载
- 积分：1
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svpwm12345
SVPWM仿真的matlab模块，仅供参考。(SVPWM simulation matlab module, for reference only.)
- 2013-03-17 15:04:09下载
- 积分：1
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fit_ML_maxwell
fit_ML_normal - Maximum Likelihood fit of the log-normal distribution of i.i.d. samples!.
Given the samples of a log-normal distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = sqrt(1/(2*pi))/(s*x)*exp(- (log(x-m)^2)/(2*s^2))
with parameters: m,s
format: result = fit_ML_log_normal( x,hAx )
input: x - vector, samples with log-normal distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
m,s - fitted parameters
CRB_m,CRB_s - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML
- 2011-02-09 19:08:34下载
- 积分：1
