搜索资源列表
trained_blindandsemi-blinddetection-matlab
- 一篇基于独立分量分析（ICA）的盲信道MATLAB程序-1 Based on Independent Component Analysis (ICA) for blind channel MATLAB program
ICA_AND_PCA
- 关于独立分量分析和主成分分析的区别，初学者可以-the difference between independent component analysis and principal component analysis ，beginner can look at it
FastICA_21
- 快速独立分量变换，可用于信号特征的提取以及端点检测等，非常好用！-Fast independent component transform, can be used for the extraction of signal characteristics, such as endpoint detection, as well as very easy to use!
FastICAECG
- 有关快速定点独立分量分离的Matlab源程序， 有很详细的说明，对于研究FastICA很有价值，-Independent component of the fast fixed-point Matlab source separation, had a very detailed descr iption of FastICA valuable for research,
kernel-ica1_2
- 核独立分量分析，一种基于核函数的独立分量分析方法-Kernel Independent Component Analysis, a Kernel-based Independent Component Analysis! !
IndependetComponentAnalysis
- 独立分量英文书籍必备.可以帮助你了解独立分量定义 解决问题 独立分量的衡量标准 以及经典算法-good book to understand ICA
myfica
- 基于不动点的快速ica算法 该算法能有效地分离独立分量-Based on fixed-point algorithm for fast ica algorithm can effectively separate the independent components
bssalgorithm
- 自己编写的，盲源分离算法仿真分析系统（图形界面）又名：独立分量分析；算法种类：自然梯度算法、投影自然梯度算法、FastICA、SOBI、NJD非正交联合对角化。信号种类： MASK：2ASK，4ASK，8ASK MPSK：2PSK、4PSK、8PSK MFSK：2FSK、4FSK、8FSK 分连续相位CPFSK和离散相位DPFSK两种 MQAM：16QAM、32QAM、64QAM、128QAM OFDM：子载波数可任意选定，映射方式有BPSK、QPSK、4QAM、16QAM、
ICALAB_last
- 独立分量ica：快速ICA算法软件包是基于MATLAB程序实现的快速定点独立成分分析和投影寻踪算法。它具有一个易于使用的图形用户界面，以及强大的计算算法。-The FastICA package is a free (GPL) MATLAB program that implements the fast fixed-point algorithm for independent component analysis and projection pursuit. It features an
Surveyonindependentcomponentanalysis
- 关于利用独立分量ICA对混和后的多声源进行分解的很好的参考文献-ICA on the use of independent component of the mixture after the decomposition of multi-source good references
jcwtlib-0.01.tar
- 独立成分分析（Independent Component Analysis, ICA）是近年来发展起来的一种有效的盲分离技术，最早是由法国学者Herault和Jutten于1986年提出。ICA方法的提出最初是用来解决“鸡尾酒会”问题，其过程可以归纳为，在源信号与传输通道参数均未知的情况下，仅根据源信号的统计特性，出现测信号恢复出源信号。ICA分析的关键在于根据一定的优化准则建立描述输出信号独立程度的优化判据，即目标函数，并设计相应的优化算法，寻求最优的分离矩阵，使得输出信号中各分量尽可能相互独
fp
- 基于ICA的独立分量分析，目标函数是负熵，快速不动点算法-ICA-based independent component analysis, the objective function is negative entropy, fast fixed-point algorithm
work
- 基于负熵的FASTICA的不动点算法，并行提取信号，独立分量分析-Based on negative entropy FASTICA the fixed point algorithm, parallel extract the signal, independent component analysis
qiaodu
- 基于峭度的独立分量分析，包括并行提取和串行提取的程序-ICA based on kurtosis analysis, including parallel and serial extraction procedures for extracting
ML
- 基于最大似然估计的独立分量分析算法，包括随机梯度算法，相对梯度算法，快速不动点算法3个程序-Based on maximum likelihood estimation of independent component analysis algorithms, including stochastic gradient algorithm, the relative gradient algorithm, fast fixed-point algorithm for three programs
FastICA_2.1
- 很好的盲信号分离软件，可以实现降维和独立分量成分的分离-A very good software for blind signal separation can be achieved dimension reduction and independent component separation of components
fasticaalgorithm
- 对接收信号进行分离是信号处理过程中经常用到的，然而在完全不知道接受信号先验知识的情况下进行分离就是所谓的盲源分离，此程序就是重多盲源分离算法中重要的一种快速独立分量分析盲源分离算法——FASTICA-Separation of the received signal is the signal processing is often used, but do not know to accept in full prior knowledge of the signal to separate
fastICA_imag
- 基于快速独立分量分析盲复原算法的混合图像分离-Independent component analysis based on fast recovery algorithm for blind separation of mixed images
q
- 基于松弛因子的快速独立分量分析算法的遥感图像分类技术-Relaxation factor based on the fast Independent Component Analysis Algorithm for remote sensing image classification techniques
libsvm-errorcode
- 新独立分量分析的算法，主要用于盲信号的分离计算-The newly independent component analysis algorithms, mainly for blind signal separation of the calculation