Process / pipelineEnsemble decomposition
CEEMDAN
完全自适应噪声集合经验模态分解（CEEMDAN）是经验模态分解（EMD）的一个改进变体，它通过自适应噪声的集合平均来解决模态混叠伪影。CEEMDAN由Torres及其同事（2011）提出，将信号分解为代表不同尺度振荡的固有模态函数（IMFs）。该方法向多个实现添加受控噪声并平均结果，产生比标准EMD更稳定、物理意义更强的分量。
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来源
- Torres, M. E., Colominas, M. A., Schlotthauer, G., & Flandrin, P. (2011). A complete ensemble empirical mode decomposition with adaptive noise. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4144–4147). DOI: 10.1109/ICASSP.2011.5947265 ↗
- Colominas, M. A., Schlotthauer, G., & Torres, M. E. (2014). Improved complete ensemble empirical mode decomposition with adaptive noise. IEEE Transactions on Signal Processing, 63(6), 1408–1413. link ↗
- 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 of London A, 454(1971), 903–995. DOI: 10.1098/rspa.1998.0193 ↗
如何引用本页
ScholarGate. (2026, June 3). Complete Ensemble Empirical Mode Decomposition with Adaptive Noise. ScholarGate. https://scholargate.app/zh/time-series/ceemdan
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 经验模态分解 (EMD)信号处理↔ compare
- 经验小波变换时间序列↔ compare
- 变分模态分解 (Variational Mode Decomposition, VMD)信号处理↔ compare