vmd
Builtin index · Signal processing
result = vmd(x, k, [alpha=2000])
Input: x, k, alpha · Output: result
What it is
x: a length-N real vector; k (number, integer): the exact number of modes to extract; alpha (optional number): bandwidth-penalty strength. Variational Mode Decomposition: decomposes x into exactly k band-limited modes by minimizing each mode's bandwidth around its own center frequency, solved by ADMM (not sifting, unlike emd).
Overloads
One call shape — this function is not overloaded.
| Code | Input | Output | Description |
result = vmd(x, k, [alpha=2000]) | x vectorx: a length-N real vector.k numberk (number, integer): the exact number of modes to extract.alpha number, optional (default 2000)alpha (optional number): bandwidth-penalty strength. | Model —kind "vmd"modes matrixa k×N matrix, one mode per rowcenter_freqs vectora length-k vector, normalized to cycles/sample — 0.5 is Nyquist | Returns a Model (kind "vmd") with fields modes (a k×N matrix, one mode per row) and center_freqs (a length-k vector, normalized to cycles/sample — 0.5 is Nyquist). |
Examples
From the documentation, and run by the build -- the output and the figure below are what the engine actually produced. Simplest first.
v = vmd(signal, 2) # exactly 2 modes
freqs_hz = v.center_freqs .* fs # convert to Hz
from numerics-dsp
How it works
vmd(x, k, [alpha=2000]) — Variational Mode Decomposition: decomposes x into exactly k band-limited modes by minimizing each mode's bandwidth around its own center frequency, solved by ADMM (not sifting, unlike emd). Model kind "vmd", fields modes (a matrix, one mode per row) and center_freqs (each mode's converged center frequency, normalized to cycles/sample — 0.5 is Nyquist).
From the note above this builtin in the interpreter, where the reasoning was written down as it was built.