Linganisha mbinu

	CEEMDAN	Uchanganuzi wa Njia Mbadala (Variational Mode Decomposition - VMD)
Nyanja	Mfululizo wa Muda	Uchakataji wa Mawimbi
Familia	Process / pipeline	Machine learning
Mwaka wa asili	2011	2014
Mwanzilishi	María E. Torres	Konstantin Dragomiretskiy & Dominique Zosso
Aina	Non-stationary signal decomposition	Adaptive variational signal decomposition algorithm
Chanzo asilia	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).	Dragomiretskiy, K., & Zosso, D. (2014). Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3), 531–544.
Majina mbadala	CEEMDAN, Ensemble EMD with noise	VMD, Adaptive Signal Decomposition, Variational Signal Decomposition, Varyasyonel Mod Ayrıştırma
Zinazohusiana	3	2
Muhtasari	Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is an improved variant of empirical mode decomposition (EMD) that addresses mode-mixing artifacts through ensemble averaging with adaptive noise. Introduced by Torres and colleagues (2011), CEEMDAN decomposes signals into intrinsic mode functions (IMFs) representing oscillations at different scales. The method adds controlled noise to multiple realizations and averages the results, producing more stable, physically meaningful components than standard EMD.	Variational Mode Decomposition (VMD) is a fully adaptive, non-recursive signal decomposition method introduced by Konstantin Dragomiretskiy and Dominique Zosso in 2014. It decomposes a real-valued input signal into a discrete number of sub-signals, called intrinsic mode functions (IMFs), each with a specific sparsity in the frequency domain. Unlike Empirical Mode Decomposition, VMD frames decomposition as a variational optimization problem solved via the Alternating Direction Method of Multipliers (ADMM), yielding robust and physically meaningful components.