เปรียบเทียบวิธี

	การสลายตัวเชิงประจักษ์ (Empirical Mode Decomposition: EMD)	การแยกโหมดแบบแปรผัน (Variational Mode Decomposition - VMD)
สาขาวิชา	การประมวลผลสัญญาณ	การประมวลผลสัญญาณ
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	1998	2014
ผู้ริเริ่ม	Norden Huang et al.	Konstantin Dragomiretskiy & Dominique Zosso
ประเภท	Adaptive data-driven decomposition algorithm	Adaptive variational signal decomposition algorithm
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	EMD, Intrinsic Mode Decomposition, Adaptive Signal Decomposition, Ampirik Mod Ayrıştırma	VMD, Adaptive Signal Decomposition, Variational Signal Decomposition, Varyasyonel Mod Ayrıştırma
ที่เกี่ยวข้อง	3	2
สรุป	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.