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分析非平稳时变的实证分解法（EMD）的基础上产生的适应性，本征模函数（IMF）的数据，处理的数据。-analyze the non-stationary time-varying data processed by the Empirical Decomposition Method (EMD), which generates the adaptive basis, Intrinsic Mode Functions (IMF), from the data.
Each chapter pa
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对一时间序列进行EMD分解，产生若干经验模态。是HHT变换的前提。-EMD of a time series decomposition, resulting in a number of empirical mode，which is a prerequisite for HHT transformation.
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快速eemd算法，收敛速度与传统eemd相比有很大改进-EMD is a nonlinear and nonstationary time domain decomposition method. It is an adaptive, data-driven algorithm
that decomposes a time series into multiple empirical modes, known as intrinsic mode functions (IMFs).
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可用于三维经验正交模态分解，直接使用，很方便啊(which can be used for Empirical Orthogonal Mode Decomposition(EMF) of three dimension)
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对信号进行EEMD分解，得到经验模态分量和一个趋势项(EEMD decomposition of the signal to obtain empirical mode components and a trend of the item)
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