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jadeall
说明: 历次进行修改过的JADE算法,可以看出盲源分离算法的演变过程,并通过对各算法的研究看出思路(all versions of the JADE algorithm.)
- 2020-09-22 14:17:51下载
- 积分:1
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detection2
针对嵌入水印后的二值图像,利用其频域特性检测隐藏信息。(embedded watermark of two binary images, using its frequency domain characteristics of detecting hidden messages.)
- 2006-07-06 13:00:47下载
- 积分:1
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power_converter1
3phase power converter using sync 6-Pulse Generator
- 2012-09-15 10:41:16下载
- 积分:1
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MATLAB-control-system
一般控制系統應用於MATLAB之指令
一般控制系統應用於MATLAB之指令(General control system used in MATLAB' s command of the general control system used in MATLAB command)
- 2011-11-28 23:08:53下载
- 积分:1
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Kalman
捷联惯导初始对准Kalman滤程序,非常难得的代码源程序。(SINS initial alignment Kalman filter program, source code is very rare.)
- 2011-12-26 18:47:21下载
- 积分:1
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fig1
一个对于光孤子的编程。程序比较简单,但是结果比较实际,推荐。(For the programming of a soliton. Procedure is simple, but the results more practical recommendation.)
- 2010-06-18 19:10:35下载
- 积分:1
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hamming_QPSK
仿真未编码和进行(7,4)汉明码编码的QPSK调制通过AWGN信道后的误比特率(Simulation is not encoded by AWGN channel bit error rate and the (7,4) Hamming code coded QPSK modulation)
- 2021-04-19 16:38:51下载
- 积分:1
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xinhaochuli-yuyin
MATLAB 处理语言信号,改变语音频率等功能(chulixinghao gaibian yuyin lsdf )
- 2011-11-02 11:48:56下载
- 积分:1
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MT1D_FWD
大地电磁一维层状介质电磁响应程序,希望能对初学者有用(Magnetotelluric one-dimensional electromagnetic response program of layered media, the hope can be useful for beginners)
- 2015-04-16 15:37:03下载
- 积分:1
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fs_sup_relieff
Relief算法中特征和类别的相关性是基于特征对近距离样本的区分能力。算法从训练集D中选择一个样本R,然后从和R同类的样本中寻找最近邻样本H,称为Near Hit,从和R不同类的样本中寻找最近样本M,称为Near Miss,根据以下规则更新每个特征的权重:
如果R和Near Hit在某个特征上的距离小于R和Near Miss上的距离,则说明该特征对区分同类和不同类的最近邻是有益的,则增加该特征的权重;反之,如果R和Near Hit在某个特征上的距离大于R和Near Miss上的距离,则说明该特征对区分同类和不同类的最近邻起负面作用,则降低该特征的权重。(The correlation between feature and category in Relief algorithm is based on distinguishing ability of feature to close sample. The algorithm selects a sample R from the training set D, and then searches for the nearest neighbor sample H from the samples of the same R, called Near Hit, and searches for the nearest sample M from the sample of the R dissimilar, called the Near Miss, and updates the weight of each feature according to the following rules:
If the distance between R and Near Hit on a certain feature is less than the distance between R and Near Miss, it shows that the feature is beneficial to the nearest neighbor of the same kind and dissimilar, and increases the weight of the feature; conversely, if the distance between R and Near Hit is greater than the distance on R and Near Miss, the feature is the same. The negative effect of nearest neighbor between class and different kind reduces the weight of the feature.)
- 2018-04-17 14:41:55下载
- 积分:1