希尔伯特-黄变换
算法
数学
托普利兹矩阵
希尔伯特变换
系列(地层学)
模式(计算机接口)
趋同(经济学)
瞬时相位
非线性系统
分解
小波
应用数学
傅里叶变换
滤波器(信号处理)
计算机科学
数学分析
纯数学
光谱密度
人工智能
白噪声
统计
操作系统
物理
生物
量子力学
经济增长
古生物学
计算机视觉
经济
生态学
作者
L. C. C. Lin,Wang Yang,Haomin Zhou
标识
DOI:10.1142/s179353690900028x
摘要
The empirical mode decomposition (EMD) was a method pioneered by (N. Huang et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear nonstationary time series analysis, Proc. Roy. Soc. Lond. A454 (1998) 903–995) as an alternative technique to the traditional Fourier and wavelet techniques for studying signals. It decomposes a signal into several components called intrinsic mode functions (IMFs), which have shown to admit better behaved instantaneous frequencies via Hilbert transforms. In this paper, we propose an alternative algorithm for EMD based on iterating certain filters, such as Toeplitz filters. This approach yields similar results as the more traditional sifting algorithm for EMD. In many cases the convergence can be rigorously proved.
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