lmd {LMD} | R Documentation |
Method for finding Product Functions (PFs)
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
)
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) |
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
list(pf,residue) | PFs:The decompose functions arranged from high frequency to low frequency | residue:residual component
Shubhra Prakash, shubhraprakash279@gmail.com
https://pypi.org/project/PyLMD/
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)