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Fully Nonparametric Regression Estimation Based on Empirical Mode Decomposition

机译:基于经验模态分解的全非参数回归估计

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Empirical Mode Decomposition (EMD) is a non-stationary signal processing method developed recently.It has been applied in many engineering fields.EMD has many similarities with wavelet decomposition.But EMD Decomposition has its own characteristics,especially in accurate trend extracting.Therefore the paper firstly proposes an algorithm of extracting slow-varying trend based on EMD.Then,according to wavelet regression estimation method,a new regression function estimation method based on EMD is presented.The simulation proves the advantages of the approach with easy computation and more accurate result.
机译:经验模态分解(Empirical Mode Decomposition,EMD)是最近发展起来的一种非平稳信号处理方法,已在许多工程领域得到应用,EMD与小波分解有很多相似之处,但是EMD分解具有其自身的特性,尤其是在精确趋势提取中。本文首先提出了一种基于EMD的慢速趋势提取算法,然后根据小波回归估计方法,提出了一种新的基于EMD的回归函数估计方法。仿真证明了该方法的优点,计算简单,计算准确。结果。

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