Bandwidth Empirical Mode Decomposition and its Application
Bandwidth Empirical Mode Decomposition and its Application
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DOI：
10.1142/s0219691308002689
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发表时间：
2008-11
期刊：
影响因子：
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通讯作者：
Qiwei Xie;B. Xuan;Silong Peng;Jianping Li;Weixuan Xu;Hua Han
中科院分区：
文献类型：
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作者：
Qiwei Xie;B. Xuan;Silong Peng;Jianping Li;Weixuan Xu;Hua Han
有一些方法可以将信号分解成不同的分量，例如：傅立叶分解和小波分解。但它们在某些方面存在局限性。近年来出现了一种新的信号分解算法-经验模式分解（EMD）算法，它为非平稳信号的自适应多尺度分析提供了有力的工具。近年来的研究表明，经验模态分解（EMD）在时间序列分解中具有显著的效果，但也存在尺度混合、收敛性差等问题。本文提出了两个关键点来设计带宽经验模态分解，以改善经验模态分解算法。通过对仿真信号和实际信号的分析，证实了利用带宽准则得到的本征模函数（IMF）能够逼近真实的分量，反映了被分析信号的本征信息。本文利用带宽经验模态分解将用电量数据分解为周期和趋势，从而识别用电量序列的结构规律。
There are some methods to decompose a signal into different components such as: Fourier decomposition and wavelet decomposition. But they have limitations in some aspects. Recently, there is a new signal decomposition algorithm called the Empirical Mode Decomposition (EMD) Algorithm which provides a powerful tool for adaptive multiscale analysis of nonstationary signals. Recent works have demonstrated that EMD has remarkable effect in time series decomposition, but EMD also has several problems such as scale mixture and convergence property. This paper proposes two key points to design Bandwidth EMD to improve on the empirical mode decomposition algorithm. By analyzing the simulated and actual signals, it is confirmed that the Intrinsic Mode Functions (IMFs) obtained by the bandwidth criterion can approach the real components and reflect the intrinsic information of the analyzed signal. In this paper, we use Bandwidth EMD to decompose electricity consumption data into cycles and trend which help us recognize the structure rule of the electricity consumption series.