Description: 经验模式分解程序（简称EMD），在某些情况下比WAVELET TRANSFORM 和STFT分析效果要好，就是计算很费时间！-Empirical Mode Decomposition (EMD), In some cases than WAVELET TRANSFORM STFT analysis and better results. Computing is a great time! Platform: |
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Author:张勇 |
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Description: 对于输入的时间序列，按照经验模式分解得到一系列基本模式分量和余项的和。经验模式分解是区别于傅里叶变换和小波变化的一种更一般的时间序列频率分解的方法- IMF = EMD(X) where X is a complex vector computes Bivariate Empirical Mode
Decomposition of X, resulting in a matrix IMF containing 1 IMF per row, the
last one being the residue. Platform: |
Size: 7168 |
Author:Miss Jin |
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Description: 经验模态分解工具箱，用于处理复杂信号的特征提取十分有效-Empirical mode decomposition toolbox for dealing with complex signal feature extraction is very effective Platform: |
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Author:hobby |
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Description: 经验模态分解(Empirical Mode Decomposition,简称EMD)法是美籍华人N. E. Huang等人于1998年提出的，适合于分析非线性、非平稳信号序列，具有很高的信噪比。该方法的关键是经验模式分解，它能使复杂信号分解为有限个本征模函数(Intrinsic Mode Function，简称IMF)，所分解出来的各IMF分量包含了原信号的不同时间尺度的局部特征信号。-Empirical Mode Decomposition method (Empirical Mode Decomposition, the EMD for short) is a chinese-american n. e. Huang et al., in 1998, is suitable for analyzing nonlinear and non-stationary signal sequence, has the very high signal-to-noise ratio.Empirical Mode decomposition is a key to this method, it can make the complex signal is decomposed into a finite number of Intrinsic Mode Function (the Intrinsic Mode Function, the IMF), the decomposition of each IMF component contains the original signals of the local characteristics of different time scales. Platform: |
Size: 103424 |
Author:徐继亚 |
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Description: 经验模态分解(Empirical Mode Decomposition,简称EMD)法是美籍华人N. E. Huang等人于1998年提出的，适合于分析非线性、非平稳信号序列，具有很高的信噪比。该方法的关键是经验模式分解，它能使复杂信号分解为有限个本征模函数(Intrinsic Mode Function，简称IMF)，所分解出来的各IMF分量包含了原信号的不同时间尺度的局部特征信号。(Empirical mode decomposition (EMD) is proposed by Chinese American N. E. Huang et al. In 1998. It is suitable for analyzing nonlinear and non-stationary signal sequences with high signal-to-noise ratio. The key of this method is empirical mode decomposition, which can make the complex signal is decomposed into a finite intrinsic mode functions (the Intrinsic Mode Function, referred to as IMF), the decomposition of the IMF component contains the local characteristics of signals with different time scales of the original signal.) Platform: |
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Author:dovemeng
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Description: 该方法的关键是经验模式分解，它能使复杂信号分解为有限个本征模函数（Intrinsic Mode Function，简称IMF），所分解出来的各IMF分量包含了原信号的不同时间尺度的局部特征信号。经验模态分解法能使非平稳数据进行平稳化处理，然后进行希尔伯特变换获得时频谱图，得到有物理意义的频率。与短时傅立叶变换、小波分解等方法相比，这种方法是直观的、直接的、后验的和自适应的，因为基函数是由数据本身所分解得到。由于分解是基于信号序列时间尺度的局部特性，因此具有自适应性。(The key of this method is empirical mode decomposition, which decomposes the complex signal into a finite number of intrinsic mode functions (IMFs). Each IMF component is decomposed into local characteristic signals of different time scales of the original signal . Empirical mode decomposition method can stabilize the non-stationary data, and then perform Hilbert transform to obtain the time-frequency spectrum to obtain the physical frequency. Compared with short-time Fourier transform, wavelet decomposition and other methods, this method is intuitive, direct, posterior and adaptive, because the basis function is derived from the data itself. Since decomposition is based on the local characteristics of the time series of the signal sequence, it is self-adaptive.) Platform: |
Size: 6144 |
Author:张鱼小丸子
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Description: 经验模态分解（Empirical Mode Decomposition，简称EMD))方法被认为是2000年来以傅立叶变换为基础的线性和稳态频谱分析的一个重大突破?，该方法是依据数据自身的时间尺度特征来进行信号分解，无须预先设定任何基函数。
该方法的关键是经验模式分解，它能使复杂信号分解为有限个本征模函数（Intrinsic Mode Function，简称IMF），所分解出来的各IMF分量包含了原信号的不同时间尺度的局部特征信号。经验模态分解法能使非平稳数据进行平稳化处理，然后进行希尔伯特变换获得时频谱图，得到有物理意义的频率。(The Empirical Mode Decomposition (EMD) method is considered as a major breakthrough in the linear and steady state spectrum analysis based on Fu Liye transform in 2000. The method is based on the time scale characteristics of the data itself to decompose the signal without setting any base functions in advance.
The key of this method is the empirical mode decomposition, which can decompose the complex signal into a limited eigenmode function (Intrinsic Mode Function, for short, IMF), and the decomposed IMF components include the local characteristic signals of the different time scales of the original signal. The empirical mode decomposition method can smooth the non-stationary data, and then obtain the time-frequency spectrum by Hilbert transform to get the physical meaning frequency.) Platform: |
Size: 395264 |
Author:Alex16 |
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Search - complex Empirical Mode Decomposition