lmd {LMD}

Local Mean Decomposition

Description

Method for finding Product Functions (PFs)

Usage

lmd(
  signal,
  include_endpoints = TRUE,
  max_smooth_iteration = 12,
  max_envelope_iteration = 200,
  envelope_epsilon = 0.01,
  convergence_epsilon = 0.01,
  max_num_pf = 8
)

Arguments

signal

Signal values (Numeric | vector)

include_endpoints

Whether to treat the endpoint of the signal as a pseudo-extreme point (Boolean)

max_smooth_iteration

Maximum number of iterations of moving average algorithm (Integer)

max_envelope_iteration

Maximum number of iterations when separating local envelope signals (Integer)

envelope_epsilon

Terminate processing when obtaining pure FM signal (Double)

convergence_epsilon

Terminate processing when modulation signal converges (Double)

max_num_pf

The maximum number of PFs generated(Integer)

Details

LMD is a method of decomposing signal into Product Functions (PFs) based on algorithm presented in Jonathan S. Smith. The local mean decomposition and its application to EEG perception data. Journal of the Royal Society Interface, 2005, 2(5):443-454

Value

list(pf,residue) | PFs:The decompose functions arranged from high frequency to low frequency | residue:residual component

Author(s)

Shubhra Prakash, [e-mail]

References

https://pypi.org/project/PyLMD/

Examples

x=1:100
y = (2 / 3 )* sin(x * 30) + (2 / 3) * sin(x * 17.5) + (4 / 5) *cos(x * 2)
plot(y,type="l")
lmd(y)