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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
ICA_algorithms_based_on_different_objective_functi
- 一共包含了5个ICA的算法,其中: fastica.m文件中的ICA算法是基于负熵的; m_fastica.m文件中的ICA算法是基于负熵的改进算法; fastica_kurt.m文件中的ICA算法是基于峭度的; fastica_ML.m文件中的ICA算法是基于互信息的; NLPCA.m文件中的ICA算法是基于非线性PCA的。-Contains a total of five ICA algorithm, in which: fastica.m file in the ICA
ICA
- 基于负熵最大的独立分量分析算法,可以将独立的混合信号分离-Based on the negative entropy of the largest independent component analysis algorithm can be a separate mixed-signal separation
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
ica
- 基于负熵的ICA算法,独立成分分析;负熵;盲信号分离;固定点-Negative entropy based ICA algorithm, independent component analysis negative entropy blind signal separation fixed point
FastICA_25
- 芬兰人海韦里恩 的基于负熵最大的固定点ICA 文件很长 有详细程序说明 很有参考价值-Finn Haiweilien largest negative entropy-based fixed-point ICA file for a long detailed descr iption of the procedures was useful
work
- 对三路信号进行分离,基于峭度和基于负熵的独立分量分析(ICA)-The three way signal separation, based on the kurtosis and independent component analysis (ICA) based on negative entropy
fastkcica
- ICA盲源分离/数字信号非高斯性最大化、负熵最大化-fastica toolbox nmf kica
FAST-ICA
- 1、对观测数据进行中心化,; 2、使它的均值为0,对数据进行白化—>Z; 3、选择需要估计的分量的个数m,设置迭代次数p<-1 4、选择一个初始权矢量(随机的W,使其维数为Z的行向量个数); 5、利用迭代W(i,p)=mean(z(i,:).*(tanh((temp) *z)))-(mean(1-(tanh((temp)) *z).^2)).*temp(i,1)来学习W (这个公式是用来逼近负熵的) 6、用对称正交法处理下W 7、归一化W(:,p)=W(:,
ICA
- 使用java实现了FastICA,但是我是将matlab的ICA函数做成了一个jar包,使用的负熵评测高斯性-Using java realize the FastICA, but I will be an ICA matlab functions into a jar, using a Gaussian negative entropy evaluation
fushangST1
- 基于负熵最大的ICA 算法在matlab运行的代码-Based on negative entropy maximum ICA algorithm in matlab code that runs
ICA
- 基于负熵的fastica算法,绝对好使!-Fastica algorithm based on negative entropy, absolutely so!
ica
- 这是一个基于极大负熵的水印嵌入图像的ica算法程序。-This is a huge negative entropy-based image watermarking ica algorithm .
ica
- 自己编写的基于负熵的ICA程序,可以多种实现故障信号的故障分离-I have written based on negative entropy ICA program, a variety of fault isolation can achieve fault signal
ICA-matlab
- ICA算法的研究可分为基于信息论准则的迭代估计方法和基于统计学的代数方法两大类,从原理上来说,它们都是利用了源信号的独立性和非高斯性。一般情况下,所获得的数据都具有相关性,所以通常都要求对数据进行初步的白化或球化处理,因为白化处理可去除各观测信号之间的相关性,从而简化了后续独立分量的提取过程,然后再用基于负熵最大的FastICA算法,即可对图像及信号进行解混。-ICA algorithm research can be divided into iterative estimation meth
ccfxmjmu
- 基于负熵最大的独立分量分析,信号处理中的旋转不变子空间法,单径或多径瑞利衰落信道仿真,含噪脉冲信号进行相关检测,ICA(主分量分析)算法和程序。- Based on negative entropy largest independent component analysis, Signal Processing ESPRIT method, Single path or multipath Rayleigh fading channel simulation, Noisy pulse corr
基于负熵的独立成分分析
- 独立成分分析 fastICA算法 负熵(Independent component analysis, fastICA algorithm, negative entropy)
ICA快速算法原理和程序
- FastICA算法是基于非高斯性最大化原则得到的一批处理算法。峭度和负熵都可以作为非高斯性的度量。(Advantages: applicable to any non-gaussian signal, blind separation algorithm with fast convergence speed and easy to use, without the need to choose the learning step, is the most widely used algorit
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)
基于负熵的快速定点迭代的ica算法源码
- 基于负熵的快速定点迭代的独立成分分析算法以及测试程序源码,算法收敛速度快,准确度高