搜索资源列表
Statistic
- 数据驱动的统计学习方法，包括PLS、PCA、ICA等，可方便进行故障诊断研究-Data-driven statistical learning methods, including PLS, PCA, ICA, can do researching on fault diagnosis
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- ：由于许多传统的去噪方法在强背景噪声情况下提取声音信号的能力变弱甚至失效， 提出 应用独立成分分析( I C A) 方法对声音信号进行特征提取， 并证明了这种 I C A 变换能增强语音和音 乐信号的超高斯性． 在此基础上， 应用 I C A基函数作为滤波器， 通过阈值化的去噪方法对含有强高 斯背景噪声的声音信号进行去噪仿真实验． 结果表明， 本方法明显优于传统的均值滤波和小波去噪 方法， 为强背景噪声下弱信号的检测提供 了新的途径．-: As many of the t
mi
- matlab 互信息理论主要函数的工具包，包括互信息，熵的计算公式等程序-the main function of mutual information theory matlab toolkit, including the mutual information, entropy calculation
Matlab-Blind-signal-separation
- 盲源分离问题的matlab程序，采用ICA方法-Matlab problem of blind source separation program, the use of ICA methods
ica
- 一独立分量分析是在研究盲源分离过程中出现的一种全新的数据分析和信号处理方法 -An independent component analysis in the study of blind source separation is the process of the emergence of a new data analysis and signal processing methods
mfile
- ICA盲源分离方法，可以很快的分离混合信号，对初学者有很大的帮助。-ICA blind source separation methods, can quickly separate mixed signal, there is a great help for beginners.
FICA
- FAST ICA使用方法和程序，用于ICA识别玉米种子图像！-FAST the ICA using the methods and procedures for the image of the ICA to identify maize seeds
Simple-Clustering
- ICA Clustering In computer science, Imperialist Competitive Algorithm (ICA)[1] is a computational method that is used to solve optimization problems of different types. Like most of the methods in the area of evolutionary computation, ICA does n
TestICA
- 基于ICA的多尺度子空间方法，在图像处理上应用时一个很好的尝试。-Multiscale subspace methods based on ICA in image processing applications is a very good attempt.
LVAICA_009
- We consider an extension of ICA and BSS for separating mutually dependent and independent components from two related data sets. We propose a new method which first uses canonical correlation analysis for detecting subspaces of independent and
kernelICA-jmlr
- We present a class of algorithms for independent component analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On the one hand, we show that our contrast functions are related to mutu
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- 齿轮箱早期的故障信号往往十分微弱,信噪比低,这大大限制了已有诊断方法在早期诊断中的应用,因此如何获取真实的振动信号是提高齿轮箱早期故障诊断质量的关键,独立分量分析(ICA)为此提供了一种新的思路。文 中研究了ICA在齿轮箱故障早期诊断中的应用,首先分析了齿轮箱的混合振动信号模型,然后针对具体的轴承故障进行了实验,并使用快速ICA算法分离出轴承的振动信号-The early gearbox fault signal is often very weak, low signal-to-noise
ICA
- In computer science, Imperialist Competitive Algorithm (ICA) is a computational method that is used to solve optimization problems of different types. Like most of the methods in the area of evolutionary computation, ICA does not need the gradient of
ICApaper
- 一篇关于ICA技术的博士论文，论文内容非常充实，对ICA的发展、原理和改进具有详细的描述。-a doctoral dissertation about ICA,the paper is rich in content, and described the development ,the principle and improwment methods.
XHQYJSDZS
- 为了快速准确地计算电噪声奇异性, 在介绍信号奇异性计算方法的基础上, 将其引入到电噪声信号分 析中, 提出一种新的基于多重分形奇异性指数计算信号电噪声的方法。新方法利用多重分形来提取电噪声中可 以表达信号内在细节特征的奇异点Hê lder指数, 通过计算电噪声中Hê lder指数的差异来进行噪声分析。通过 对实测电迁移和光耦电噪声的计算分析表明, 电迁移后期奇异性指数会发生突变 而良品光耦器件和次品光耦 器件在信号噪声的平均H ê lder指数方面差异明显。实验结果证明本方法是一
19870363BSS
- 盲分离程序，利用ICA独立分量分析的方法实现了线性混合信号的盲分离-Blind separation procedures, the use of independent component analysis ICA blind separation methods to achieve linear mixed signal
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- 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。-ICA algorithm research can be divided into iterative estimation method based on information criterion and based on statistical algebraic methods into two categories, in principle, th
work
- 通过快速ica方法与数学形态学相结合，实现图像的分割。-Ica by rapid methods combined with mathematical morphology, to achieve image segmentation.
jangiu
- 可以广泛的应用于数据预测及数据分析,用谱方法计算流体力学一些流动现象的整体稳定性,ICA（主分量分析）算法和程序。- Can be widely used in data analysis and forecast data, Spectral methods of computational fluid dynamics flow of some of the overall stability of the phenomenon, ICA (Principal Component Analy
material-of-book
- Independent component analysis (ICA) is a method for finding underlying factors or components multivariate (multidimensional) statistical data. What distinguishes ICA other methods is that it looks for components that are both statistically ind