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AHPMOHU
模糊层次分析法的原始程序。。。。。。。。。。。。。。。。。。(FAHP the original program. . . . . . . . . . . . . . . . . .)
- 2009-12-15 15:04:49下载
- 积分：1
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LMSmatlab
关于lms的matlab仿真图，以及改进算法(Lms matlab simulation on the map, and the improved algorithm)
- 2013-12-21 14:30:13下载
- 积分：1
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MIMLBoost
matlab编写的MIMLBoosting算法，其中包括实现算法的函数，还有可作为示例的数据和主程序，可以运行，可以对多示例多标记样本进行分类，并计算正确率等多个指标。(MIMLBoosting algorithm matlab prepared, including the implementation of the function of the algorithm, as well as examples of data and the main program, you can run, you can multi-instance multi-label classification of samples and calculate the number of indicators, such as the correct rate.)
- 2014-01-15 15:12:17下载
- 积分：1
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surshrHEDA
基于直方图的分布估计算法matlab源程序，测试函数选用的是30维函数(for h=1:m
for j=1:((x2-x1)/binswidth)
if (x(h)>=x1+(j-1)*binswidth)&(x(h)<x1+j*binswidth)
H(j)=H(j)+1
end
end
end
[wid,len]=size(H)
H=H/(wid*len)
objvalue=sort(objvalue, descend )
for j=1:((x2-x1)/binswidth))
- 2013-03-15 10:44:15下载
- 积分：1
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brain-images
dataset for brain images
- 2015-04-20 13:43:09下载
- 积分：1
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BracketExactrar
finds the left end of the interval containing a point and valuates the exact solution for 1D finite element.
- 2011-01-21 20:55:59下载
- 积分：1
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tracking_optical-flow
This demo tracks cars in a video by detecting motion using optical flow. The cars are segmented from the background by thresholding the motion vector magnitudes. Then, blob analysis is used to identify the cars.
- 2013-07-24 12:59:47下载
- 积分：1
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Main_grating
超结构光纤光栅基本特性，利用matlab进行仿真模拟，计算反射谱和投射普(Super-structure fiber grating basic characteristics
)
- 2012-04-14 15:08:47下载
- 积分：1
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ADHDP
自适应动态规划控制参数调整,先进的自适应 ,能够自动调节 器的 ,有很好的鲁棒性 (Adaptive control parameter adjusting dynamic programming, advanced adaptive, automatically regulator, has good robustness)
- 2017-03-27 15:18:36下载
- 积分：1
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fit_ML_normal
fit_ML_normal - Maximum Likelihood fit of the normal distribution of i.i.d. samples!.
Given the samples of a normal distribution, the PDF parameter is found
fits data to the probability of the form:
p(r) = sqrt(1/2/pi/sig^2)*exp(-((r-u)^2)/(2*sig^2))
with parameters: u,sig^2
format: result = fit_ML_normal( x,hAx )
input: x - vector, samples with 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
sig^2,u - fitted parameters
CRB_sig2,CRB_u - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML
- 2011-02-09 19:09:33下载
- 积分：1
