經驗模態分解法之研究趨勢探討

Survey on the Recent Developments of Empirical Mode Decomposition

10.29768/JNTUT.200906.0003

吳順德(Shuen-De Wu)；陳思予(Szu-Yu Chen)；陳虹伯(Hung-Bo Chen)

經驗模態分解法 ； 多尺度 ； 非穩態 ； empirical mode decomposition ； multi-scale ； non-stationary

臺北科技大學學報

42卷1期（2009 / 06 / 01）

31 - 50

繁體中文

訊號處理在科學以及工程領域上皆是很重要的課題。自然界的訊號，大多都是非穩態（時變）、非線性過程，故往往得到的訊息包含了雜訊的部分。傳統的傅立葉轉換，其處理的訊號限制為：線性、穩態過程，所以並不能處理大部分的訊號特性。經驗模態分解法(Empirical Mode Decomposition, EMD)對於非線性、非穩態訊號提供一種多尺度、適應性的解析方式，這個方法大大改善了上述的限制。本研究針對多位學者提出此演算法三大課題的改進方式：停止準則、包絡線與邊界效應，進行歸納與比較。另外，簡單介紹有關EMD基底的正交性條件、分解上的限制以及基底重建問題。

Signal processing is very important for science and engineering researches. Real world signals are often noisy, non-stationary, and obtained from nonlinear systems. However, the majority of signal processing algorithms proposed in the literature such as Fourier transform are better suited for analyzing the linear stationary signals with weak noise. Empirical mode decomposition (EMD) provides a powerful tool for adaptive multi-scale analysis of nonlinear and non-stationary signals. In this paper, the proposed improvement ways of three main topics on this algorithm including stopping criterion, envelope estimation and boundary effect, were summarized and compared. In addition, we make a brief introduction involving orthogonality condition of basis functions, the limitation of decomposition capacity and reconstruction issue of basis functions.