eemd优缺点_超赞的EEMD程序,没有问题「终于解决」

%function allmode=eemd(Y,Nstd,NE)

%

% This is an EMD/EEMD program

%

% INPUT:

% Y: Inputted data;1-d data only

% Nstd: ratio of the standard deviation of the added noise and that of Y;

% NE: Ensemble number for the EEMD

% OUTPUT:

% A matrix of N*(m+1) matrix, where N is the length of the input

% data Y, and m=fix(log2(N))-1. Column 1 is the original data, columns 2, 3, …

% m are the IMFs from high to low frequency, and comlumn (m+1) is the

% residual (over all trend).

%

% NOTE:

% It should be noted that when Nstd is set to zero and NE is set to 1, the

% program degenerates to a EMD program.(for EMD Nstd=0,NE=1)

% This code limited sift number=10 ,the stoppage criteria can’t change.

%

% References:

% Wu, Z., and N. E Huang (2008),

% Ensemble Empirical Mode Decomposition: a noise-assisted data analysis method.

% Advances in Adaptive Data Analysis. Vol.1, No.1. 1-41.

%

% code writer: Zhaohua Wu.

% footnote:S.C.Su 2009/03/04

%

% There are three loops in this code coupled together.

% 1.read data, find out standard deviation ,devide all data by std

% 2.evaluate TNM as total IMF number–eq1.

% TNM2=TNM+2,original data and residual included in TNM2

% assign 0 to TNM2 matrix

% 3.Do EEMD NE times————————————————————-loop EEMD start

% 4.add noise

% 5.give initial values before sift

% 6.start to find an IMF————————————————IMF loop start

% 7.sift 10 times to get IMF————————–sift loop start and end

% 8.after 10 times sift –we got IMF

% 9.subtract IMF from data ,and let the residual to find next IMF by loop

% 6.after having all the IMFs———————————————IMF loop end

% 9.after TNM IMFs ,the residual xend is over all trend

% 3.Sum up NE decomposition result————————————————-loop EEMD end

% 10.Devide EEMD summation by NE,std be multiply back to data

%

% Association: no

% this function ususally used for doing 1-D EEMD with fixed

% stoppage criteria independently.

%

% Concerned function: extrema.m

% above mentioned m file must be put together

function allmode=eemd(Y,Nstd,NE)

%part1.read data, find out standard deviation ,devide all data by std

xsize=length(Y);

dd=1:1:xsize;

Ystd=std(Y);

Y=Y/Ystd;

%part2.evaluate TNM as total IMF number,ssign 0 to TNM2 matrix

TNM=fix(log2(xsize))-1;

TNM2=TNM+2;

for kk=1:1:TNM2,

for ii=1:1:xsize,

allmode(ii,kk)=0.0;

end

end

%part3 Do EEMD —–EEMD loop start

for iii=1:1:NE, %EEMD loop -NE times EMD sum together

%part4 –Add noise to original data,we have X1

for i=1:xsize,

temp=randn(1,1)*Nstd;

X1(i)=Y(i)+temp;

end

%part4 –assign original data in the first column

for jj=1:1:xsize,

mode(jj,1) = Y(jj)