搜索资源列表
rwwep
- 经典的灰度共生矩阵纹理计算方法，抑制载波型差分相位调制，独立成分分析算法降低原始数据噪声。- Classic GLCM texture calculation method, Suppressed carrier type differential phase modulation, Independent component analysis algorithm reduces the raw data noise.
yr770
- 独立成分分析算法降低原始数据噪声，Matlab实现界面友好，可以提取一幅图中想要的目标。- Independent component analysis algorithm reduces the raw data noise, Matlab to achieve user-friendly, Target can be extracted in a picture you want.
mpsca
- 仿真效果非常好，独立成分分析算法降低原始数据噪声，用MATLAB编写的遗传算法路径规划。- Simulation of the effect is very good, Independent component analysis algorithm reduces the raw data noise, Genetic algorithms using MATLAB path planning.
FastICA
- 盲源分离独立成分分析算法中的快速独立成分分析算法，可以直接带入图片进行运行- U76F2 u6E90 u5206 u7BB u72E u72E u7C2 u7129 U53EF u4E5 u76F4 u63A5 u5E26 u5165 u56FE u7257 u8FDB u884C u8FD0 u884C
fastica
- 脑电波信号处理，采用独立成分分析法的数据处理(EEG signal processing)
avgorithm_algorithm_language
- FastICA算法,使用matlab语言,是非常好的独立成分分析工具,,(FastICA algorithm, the use of matlab language, independent component analysis tool, is very good,)
FastICA
- 独立成分分析matlab代码，进行特征降维与特征选择(ICA transform for feature selection)
6603087ICAbss
- 计算独立成分分析算法的matlab程序，通过串音误差来分析算法的收敛度。(The matlab program of independent component analysis algorithm is calculated, and the convergence of the algorithm is analyzed by crosstalk error.)
pca_ica
- 可以实现主成分分析（PCA）和独立成分分析（ICA）。(This package contains functions that implement Principal Component Analysis (PCA) and Independent Component Analysis (ICA).)
实验6_NMF-ICA
- 实现非负矩阵分解算法和独立成分分析，得到遥感遥感图像解混结果(The non negative matrix factorization algorithm and independent component analysis are implemented to get the unmixing results of remote sensing images)
MIMO_ICA
- 基于独立成分分析（ICA）的盲源分离，对MIMO进行信号检测。在信道条件未知的情况下将两路信号分离(Blind source separation based on independent component analysis (ICA) is used to detect MIMO signals. Two channel signals are separated in the case of unknown channel conditions)
123FastICA_2.5
- 盲源分离，独立成分分析，好用的独立成分分析程序(Blind source separation)
ICA工具箱 - FastICA_25
- 独立成分分析（Independent Component Analysis），最早应用于盲源信号分离（Blind Source Separation，BBS）。ICA理论的基本思想是从一组混合的观测信号中分离出独立信号，或者尽可能独立的信号对其他信号进行表征。(Independent Component Analysis was first applied to Blind Source Separation (BBS). The basic idea of ICA theory is to s
基于负熵的FastICA
- 独立成分分析的Fast-ICA算法.可用于图像处理、信号分析、模式识别、人工智能(independent component analysis method based on negentropy.It can be used in image processing, signal analysis, pattern recognition and artificial intelligence)
kernel-ica1_2.tar
- 独立成分分析 用于信号去噪 盲源信号处理 独立源提取(independent component analysis)
eeglab13_5_4b
- eeglab提供了一个交互式的图形用户界面（GUI）允许用户灵活、交互过程的高密度脑电图或其他动态脑数据利用独立成分分析（ICA）和/或时间/频率分析（TFA），以及标准的平均方法。eeglab还集成了泛的教程和明窗，加上一个命令历史记录功能，简化了用户从基于GUI的数据探索建立和运行批处理或自定义数据分析脚本(EEGLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and in
FastICA_25[1].tar
- fastica，独立成分分析，用于信号分离。(FastICA, independent component analysis, used for signal separation.)
FastICA独立成分分析
- 可对信号进行快速分离，算法优于传统ICA。(The signal can be quickly separated, the algorithm is superior to the traditional ICA.)
FastICA_25
- 独立成分分析是近年来提出的非常有效的数据分析工具，它主要用来从混合数据中提取出原始的独立信号。(Independent component analysis (independent component analysis) is a very effective data analysis tool in recent years. It is mainly used to extract original independent signals from mixed data.)
PCA 和ICA
- 主成分分析和独立成分分析算法，可用于数据降维