集成经验模式分解eemdmatlab代码实现-白红宇

集成经验模式分解eemdmatlab代码实现

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发布时间：2019-05-24

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之前在CSDN上下载这些东西要积分，下载不了，所以搞了个这样的，亲测有效

集成经验模式分解eemd,一种改进的emd的信号分解方法matlab代码实现

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; Nstd = (0.1 ~ 0.4)std(Y).
% NE: Ensemble number for the EEMD, NE = 10-50.
% 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

% 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 N*TNM2 matrix

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

TNM2=TNM+2;

for kk=1:1:TNM2,

for ii=1:1:xsize,

allmode(ii,kk)=0.0;

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; % add a random noise to Y

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

end

%part4 --assign original data in the first column

for jj=1:1:xsize,

mode(jj,1) = Y(jj); % assign Y to column 1of mode

end

%part5–give initial 0 to xorigin and xend

xorigin = X1; %

xend = xorigin; %

%part6–start to find an IMF-----IMF loop start

nmode = 1;

while nmode <= TNM,     xstart = xend; %last loop value assign to new iteration loop                %xstart -loop start data     iter = 1;      %loop index initial value

%part7–sift 10 times to get IMF—sift loop start

while iter<=10,

[spmax, spmin, flag]=extrema(xstart); %call function extrema

%the usage of spline ,please see part11.

upper= spline(spmax(:,1),spmax(:,2),dd); %upper spline bound of this sift

lower= spline(spmin(:,1),spmin(:,2),dd); %lower spline bound of this sift

mean_ul = (upper + lower)/2; %spline mean of upper and lower

xstart = xstart - mean_ul; %extract spline mean from Xstart

iter = iter +1;

end

%part8–subtract IMF from data ,then let the residual xend to start to find next IMF

xend = xend - xstart;