方法对比
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| 傅里叶变换与谱分析 (FFT)× | 希尔伯特-黄变换× | |
|---|---|---|
| 领域 | 信号处理 | 信号处理 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1965 | 1998 |
| 提出者≠ | James Cooley & John Tukey (FFT) | Norden Huang et al. |
| 类型≠ | Frequency-domain decomposition algorithm | Adaptive time-frequency analysis method |
| 开创性文献≠ | Cooley, J. W., & Tukey, J. W. (1965). An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation, 19(90), 297–301. DOI ↗ | Huang, N. E., et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A, 454(1971), 903–995. DOI ↗ |
| 别名 | Fast Fourier Transform, Discrete Fourier Transform, Spectral Analysis, Fourier Dönüşümü | HHT, EMD-Hilbert Spectral Analysis, Hilbert Spektral Analizi, Adaptive Time-Frequency Decomposition |
| 相关 | 2 | 2 |
| 摘要≠ | The Fourier Transform decomposes a time-domain signal into its constituent sinusoidal frequencies, revealing the spectral content hidden within complex waveforms. Joseph Fourier introduced the continuous transform in 1822, but the computationally efficient Fast Fourier Transform (FFT) was formalized by James Cooley and John Tukey in 1965. Their landmark algorithm reduced the computational complexity from O(N²) to O(N log N), making large-scale spectral analysis practical across engineering, physics, and data science. | The Hilbert-Huang Transform (HHT) is an adaptive, data-driven method for analyzing non-linear and non-stationary time series, introduced by Norden E. Huang and colleagues in 1998. It combines Empirical Mode Decomposition (EMD), which decomposes a signal into intrinsic mode functions (IMFs), with the Hilbert spectral analysis to produce instantaneous frequency and amplitude representations without assuming signal stationarity or linearity. |
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