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[digital signal] based on MATLAB CEEMD digital signal decomposition [matlab source code 1383]

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Two, some source code

clc; clear; close all t=1/1000:1/1000:1;
x=sin(20*pi*t)+sin(40*pi*t)+2*sin(60*pi*t);
imf=ceemd(x,0.02.50.2); %----------------IMF show ---------------- figure(1);
imfn=imf';
n=size(imfn,1);
subplot(n,1.1); plot(t,x); % Original signal ylabel('Raw signal'.'fontsize'.12.'fontname'.'Song in Chinese Language');
for n1=1:n- 1
    subplot(n,1,n1+1); plot(t,imfn(n1,:)); % the IMF component ylabel (['IMF' int2str(n1)]);
end


figure(2);
imfn=imf';
n=size(imfn,1);
subplot(n,1.1);
[f,z]=COMPUTE_FFT(t,x);
plot(f(1:50),z(1:50)); % Original signal ylabel('Raw signal'.'fontsize'.12.'fontname'.'Song in Chinese Language');
for n1=1:n- 1
    subplot(n,1,n1+1);
    [f,z]=COMPUTE_FFT(t,imfn(n1,:));

    plot(f(1:50),z(1:50)); % the IMF component ylabel (['IMF' int2str(n1)]);
end



ylabel('RES');

 xlabel('time \ itt/s'.'fontsize'.12.'fontname'.'Song in Chinese Language');
 % Y: Inputted data;
% Nstd: ratio of the standard deviation of the added noise and that of Y;
% NE: Ensemble member being used
% TNM: total number of modes (not including the trend)
%
function allmode=ceemd(Y,Nstd,NE,TNM)
% find data length
xsize=length(Y);
dd=1:1:xsize;
% Nornaliz data
Ystd=std(Y);
Y=Y/Ystd;
% Initialize saved data
TNM2=TNM+2;
for kk=1:1:TNM2,
    for ii=1:1:xsize,
        allmode(ii,kk)=0.0;
    end
end

for iii=1:1:NE
% adding noise
    for i=1:xsize,
        temp=randn(1.1)*Nstd;
        X1(i)=Y(i)+temp;
        X2(i)=Y(i)-temp;
    end

    % sifting X1
    xorigin = X1;
    xend = xorigin;
% save the initial data into the first column
    for jj=1:1:xsize
        mode(jj,1) = xorigin(jj);
    end
    nmode = 1;
    while nmode <= TNM,
         xstart = xend;
        iter = 1;
        while iter<=5,
             [spmax, spmin, flag]=extrema(xstart);
             upper= spline(spmax(:,1),spmax(:,2),dd);
             lower= spline(spmin(:,1),spmin(:,2),dd);
             mean_ul = (upper + lower)/2;
             xstart = xstart - mean_ul;
             iter = iter +1;
        end
        xend = xend - xstart;
        nmode=nmode+1;
        % save a mode
        for jj=1:1:xsize,
            mode(jj,nmode) = xstart(jj);
        end
    end
    % save the trend
    for jj=1:1:xsize,
        mode(jj,nmode+1)=xend(jj);
    end
    % add mode to the sum of modes from earlier ensemble members
%     1,size(allmode),2,size(mode)
    allmode=allmode+mode;

   %%%=============================================================
   % sifting X2
   xorigin = X2;
   xend = xorigin;
   % save the initial data into the first column
   for jj=1:1:xsize,
        mode(jj,1) = xorigin(jj);
   end
   nmode = 1;
   while nmode <= TNM,
       xstart = xend;
       iter = 1;
       while iter<=5,
           [spmax, spmin, flag]=extrema(xstart);
           upper= spline(spmax(:,1),spmax(:,2),dd);
           lower= spline(spmin(:,1),spmin(:,2),dd);
           mean_ul = (upper + lower)/2;
           xstart = xstart - mean_ul;
           iter = iter +1;
       end
       xend = xend - xstart;
       nmode=nmode+1;
       % save a mode
       for jj=1:1:xsize,
           mode(jj,nmode) = xstart(jj);
       end
   end
    % save the trend
    for jj=1:1:xsize,
        mode(jj,nmode+1)=xend(jj);
    end
    % add mode to the sum of modes from earlier ensemble members
    
    allmode=allmode+mode;
    %fprintf(The '-');
end
% ensemble average
allmode=allmode/NE/2;
% Rescale mode to origional unit.
allmode=allmode*Ystd;
Copy the code

3. Operation results

Matlab version and references

1 matlab version 2014A

2 Reference [1] Shen Zaiyang. Proficient in MATLAB Signal Processing [M]. Tsinghua University Press, 2015. [2] GAO Baojian, PENG Jinye, WANG Lin, PAN Jianshou. Signal and System — Analysis and Implementation using MATLAB [M]. Tsinghua University Press, 2020. [3] WANG Wenguang, WEI Shaoming, REN Xin. MATLAB Implementation of Signal Processing and System Analysis [M]. Publishing House of Electronics Industry, 2018.