Machine learningTime-frequency analysis
变分模态分解 (Variational Mode Decomposition, VMD)
变分模态分解 (VMD) 是一种完全自适应、非递归的信号分解方法，由 Konstantin Dragomiretskiy 和 Dominique Zosso 于 2014 年提出。它将实值输入信号分解为离散数量的子信号，称为本征模态函数 (intrinsic mode functions, IMFs)，每个 IMF 在频域内具有特定的稀疏性。与经验模态分解 (Empirical Mode Decomposition, EMD) 不同，VMD 将分解构建为一个变分优化问题，通过交替方向乘子法 (Alternating Direction Method of Multipliers, ADMM) 求解，从而得到鲁棒且具有物理意义的分量。
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来源
- Dragomiretskiy, K., & Zosso, D. (2014). Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3), 531–544. DOI: 10.1109/TSP.2013.2288675 ↗
如何引用本页
ScholarGate. (2026, June 2). Variational Mode Decomposition (VMD). ScholarGate. https://scholargate.app/zh/signal-processing/variational-mode-decomposition
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