Comparer des méthodes

	Décomposition Modale Empirique (DME)	Décomposition Variationnelle par Modes (VMD)
Domaine	Traitement du signal	Traitement du signal
Famille	Machine learning	Machine learning
Année d'origine	1998	2014
Auteur d'origine	Norden Huang et al.	Konstantin Dragomiretskiy & Dominique Zosso
Type	Adaptive data-driven decomposition algorithm	Adaptive variational signal decomposition algorithm
Source fondatrice	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.	Dragomiretskiy, K., & Zosso, D. (2014). Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3), 531–544.
Alias	EMD, Intrinsic Mode Decomposition, Adaptive Signal Decomposition, Ampirik Mod Ayrıştırma	VMD, Adaptive Signal Decomposition, Variational Signal Decomposition, Varyasyonel Mod Ayrıştırma
Apparentées	3	2
Résumé	Empirical Mode Decomposition (EMD) is a fully data-driven, adaptive method for decomposing nonlinear and non-stationary time series into a finite set of oscillatory components called Intrinsic Mode Functions (IMFs), plus a monotonic residue. Introduced by Norden E. Huang and colleagues at NASA in 1998, EMD requires no predefined basis functions and derives all components directly from the signal itself, making it fundamentally different from Fourier or wavelet transforms.	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.