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fastica
- 用matlab实现的最大化负熵的独立分量分析方法，作了正交化处理，可以同时分离出所有的独立分量（无噪声条件下）-Using matlab to achieve the maximization of the negentropy method of independent component analysis, orthogonal made of processing, can be isolated from all of the independent component (no nois
BSS_Demo4SP_20Mar2k5
- 定点频域ICA，使用高斯函数、负熵最大化来处理语音信号分离问题的演示-FIXED-POINT FREQUENCY DOMAIN ICA with GENERALIZED GAUSSIAN FUNCTION BASED NEGENTROPY APPROXIMATION for SPEECH SIGNAL SEPARATION
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- 【 摘 要】 提出了一种基于负熵的快速不动点1 C A算法， 介绍了负熵的定义和如何将其用作代价函数度量混合信号的非 高斯性。 详细介绍了基于负熵的固定点准则以及简化算法。实验选取 3个非高斯向量作为信号源进行 Ma t l a b分离仿真， 结果显示 分离效果良好。-Abstract Based on the rapid Negentropy 1 CA fixed point algorithm, introduced the definition of negentropy an
negentest_01_defl_gauss
- A Deflationary fastICA using negentropy approximation implementted in Matlab
negentropy_grad
- 基于负熵的盲源分离源程序,对盲源分离领域属于最基本的程序-BSS based on negentropy
FastICA_negentropy
- 基于负熵的盲源分离，使用FastICA技术，适合初学者-BSS based on negentropy by FastICA
negentropy
- 基于负熵的盲源分离算法，可以用于语音的盲分离，提高语音信号质量-Negative entropy algorithm based on blind source separation, can be used to blind separation of speech, improve the quality of voice signal
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)
MIToolbox
- 独立分量分析是一种统计和计算技术，用于揭示随机变量、测量数据或信号中的隐藏成分。(In ICA, multi-dimensional data is decomposed into components that are maximally independent in an appropriate sense (kurtosis and negentropy, in this package).the ICA components have maximal statistical indepe