Σύγκριση μεθόδων

	Παραλλακτική Αποσύνθεση Τρόπων (VMD)	Εμπειρική Αποσύνθεση Τρόπων (EMD)
Πεδίο	Επεξεργασία Σήματος	Επεξεργασία Σήματος
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2014	1998
Δημιουργός	Konstantin Dragomiretskiy & Dominique Zosso	Norden Huang et al.
Τύπος	Adaptive variational signal decomposition algorithm	Adaptive data-driven decomposition algorithm
Θεμελιώδης πηγή	Dragomiretskiy, K., & Zosso, D. (2014). Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3), 531–544.	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.
Εναλλακτικές ονομασίες	VMD, Adaptive Signal Decomposition, Variational Signal Decomposition, Varyasyonel Mod Ayrıştırma	EMD, Intrinsic Mode Decomposition, Adaptive Signal Decomposition, Ampirik Mod Ayrıştırma
Συναφείς	2	3
Σύνοψη	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.	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.