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FastICA25

于 2010-05-29 发布文件大小:65KB

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代码说明：

此为智能技术中ICA的代码，快速IcA的代码(This is smart technology ICA' s code, the code quickly IcA)

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TD-SCDMA
matlab 仿真的还可以大家分享我也是最近做这个发现的希望大家共同进步(GOOD )

2009-04-14 19:38:38下载

积分：1
MATLAB
基于MATLAB的GPS专业知识的实现（内含5个以上的源代码）(MATLAB-based implementation of GPS expertise (containing more than 5 of the source code))

2009-12-30 11:09:34下载

积分：1
BP-MATLAB
一个基于BP神经网络的matlab程序可以实现对几种字体0-9的数字识别这个文件训练网络的压缩包(Based on BP neural network matlab program can achieve several fonts on the figure 0-9 to identify the document training network packet compression)

2009-03-21 17:47:09下载

积分：1
John-Carter[2012]BRRip-XviD-ETRG
Its a great tool to getting started with the new John Carter software for source code.

2014-09-29 09:29:11下载

积分：1
PSO
PSO源代码，求解单目标优化问题，本代码用于求解非线性方程组问题(PSO source code)

2014-11-30 21:54:15下载

积分：1
field_II
说明： MATLAB的学习，以及仿真B超的field II 软件以及使用说明书(MATLAB )

2010-04-07 15:27:22下载

积分：1
NR
说明： newton raphson matlab code

2012-04-21 22:35:32下载

积分：1
kmeans
function [L,C] = kmeans(X,k) KMEANS Cluster multivariate data using the k-means++ algorithm. [L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class label per column in X and a size(X,1)-by-k matrix C containing the centers corresponding to each class. Version: 07/08/11 Authors: Laurent Sorber (Laurent.Sorber@cs.kuleuven.be) References: [1] J. B. MacQueen, "Some Methods for Classification and Analysis of MultiVariate Observations", in Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, L. M. L. Cam and J. Neyman, eds., vol. 1, UC Press, 1967, pp. 281-297. [2] D. Arthur and S. Vassilvitskii, "k-means++: The Advantages of Careful Seeding", Technical Report 2006-13, Stanford InfoLab, 2006.

2011-12-22 19:38:16下载

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Image-feature-extraction-an-overview
Image-feature-extraction-an overview

2007-11-24 11:56:54下载

积分：1
NeighborhoodRoughSetBasedFeatureEvaluationAndReduc
基于特征评估及约简的邻居粗糙集方法，可以用于粗糙集属性约简(Feature-based assessment and neighbors rough set reduction methods )

2010-09-17 10:05:26下载

积分：1

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FastICA25

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