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FastICA_2.5
fast ica 算法
matlab源代码，比较好，简单易懂(fast ica)
- 2013-10-02 14:25:23下载
- 积分：1
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多机器人系统的群集编队控制理论仿真程序
多机器人系统的集群控制仿真实现，其中运用势场法实现了集群的稳定(Realization of Cluster Control Simulation for Multi-Robot System)
- 2020-06-22 17:40:02下载
- 积分：1
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sift
基于SIFT特征跟踪的的图像稳定技术研究，我的毕业设计，附带MATLAB源代码及图片(SIFT-based feature tracking of image stabilization technology, for my graduation, with MATLAB source code and pictures)
- 2010-07-28 22:06:29下载
- 积分：1
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MatLab.7.Programming
MATLAB ７电子教程，英文。不可多得的学习资料，学习自控的必备工具书(MATLAB 7 e-tutorials and English. The rare learning materials, learning self-control essential tool for the book)
- 2009-10-16 15:18:24下载
- 积分：1
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circonvolve.m
Computes the circular convolution of two functions.
- 2009-11-27 14:02:56下载
- 积分：1
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IAR_BEFORE_START
IAR启动分析 在main()之前，IAR都做了啥(IAR start the analysis in main (before), IAR have done what)
- 2014-12-01 23:55:27下载
- 积分：1
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Matlab_Tutorial
matlab程序设计教程，供初学者最快熟悉MATLAB基本操作。(matlab programming tutorial for beginners the basic operation of the fastest growing familiar with MATLAB.)
- 2009-05-05 09:37:28下载
- 积分：1
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ray_fading
统计瑞利衰落特性,得到其幅度密度函数图。更进一步了解瑞利衰落特性。(Analyse the character of rayli fading,getting the CDF of teh rayli fadign.)
- 2013-11-21 16:18:03下载
- 积分：1
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Matlab-image-processing-
《matlab图像处理与界面编程宝典》秦襄培 编著，第2-第9章的MATLAB源代码程序，对现有代码补充完善(Matlab image processing and interface programming Collection" Qin Xiang Pei-ed,from chapter 2 to 9 of the MATLAB source code program)
- 2011-06-25 11:30:06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
