时序分解 | MATLAB实现CEEMDAN+SE自适应经验模态分解+样本熵计算
目录
效果一览
基本介绍
MATLAB实现CEEMDAN+SE自适应经验模态分解+样本熵计算
包括频谱图
附赠案例数据 可直接运行
直接替换excel数据即可使用 适合新手小白
程序设计
- 完整源码和数据获取方式：私信回复MATLAB实现CEEMDAN+SE自适应经验模态分解+样本熵计算。
clike
function [modes its]=ceemdan(x,Nstd,NR,MaxIter)
%----------------------------------------------------------------------
% INPUTs
% x: signal to decompose
% Nstd: noise standard deviation
% NR: number of realizations
% MaxIter: maximum number of sifting iterations allowed.
%
% OUTPUTs
% modes: contain the obtained modes in a matrix with the rows being the modes
% its: contain the sifting iterations needed for each mode for each realization (one row for each realization)
% -------------------------------------------------------------------------
% Syntax
%
% modes=ceemdan(x,Nstd,NR,MaxIter)
% [modes its]=ceemdan(x,Nstd,NR,MaxIter)
%
%--------------------------------------------------------------------------
% This algorithm was presented at ICASSP 2011, Prague, Czech Republic
% Plese, if you use this code in your work, please cite the paper where the
% algorithm was first presented.
% If you use this code, please cite:
%
% M.E.TORRES, M.A. COLOMINAS, G. SCHLOTTHAUER, P. FLANDRIN,
% "A complete Ensemble Empirical Mode decomposition with adaptive noise,"
% IEEE Int. Conf. on Acoust., Speech and Signal Proc. ICASSP-11, pp. 4144-4147, Prague (CZ)
%
% -------------------------------------------------------------------------
% Date: June 06,2011
% Authors: Torres ME, Colominas MA, Schlotthauer G, Flandrin P.
% For problems with the code, please contact the authors:
% To: macolominas(AT)bioingenieria.edu.ar
% CC: metorres(AT)santafe-conicet.gov.ar
% -------------------------------------------------------------------------
x=x(:)';
desvio_x=std(x);
x=x/desvio_x;
modes=zeros(size(x));
temp=zeros(size(x));
aux=zeros(size(x));
acum=zeros(size(x));
iter=zeros(NR,round(log2(length(x))+5));
for i=1:NR
white_noise{i}=randn(size(x));%creates the noise realizations
end;
for i=1:NR
modes_white_noise{i}=emd(white_noise{i});%calculates the modes of white gaussian noise
end;
for i=1:NR %calculates the first mode
temp=x+Nstd*white_noise{i};
[temp, o, it]=emd(temp,'MAXMODES',1,'MAXITERATIONS',MaxIter);
temp=temp(1,:);
aux=aux+temp/NR;
iter(i,1)=it;
end;
modes=aux; %saves the first mode
k=1;
aux=zeros(size(x));
acum=sum(modes,1);
while nnz(diff(sign(diff(x-acum))))>2 %calculates the rest of the modes
for i=1:NR
tamanio=size(modes_white_noise{i});
if tamanio(1)>=k+1
noise=modes_white_noise{i}(k,:);
noise=noise/std(noise);
noise=Nstd*noise;
try
[temp, o, it]=emd(x-acum+std(x-acum)*noise,'MAXMODES',1,'MAXITERATIONS',MaxIter);
temp=temp(1,:);
catch
it=0;
temp=x-acum;
end;
else
[temp, o, it]=emd(x-acum,'MAXMODES',1,'MAXITERATIONS',MaxIter);
temp=temp(1,:);
end;
aux=aux+temp/NR;
iter(i,k+1)=it;
end;
modes=[modes;aux];
aux=zeros(size(x));
acum=zeros(size(x));
acum=sum(modes,1);
k=k+1;
------------------------------------------------
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原文链接：https://blog.csdn.net/kjm13182345320/article/details/119920826参考资料
[1] https://blog.csdn.net/kjm13182345320/article/details/129215161
[2] https://blog.csdn.net/kjm13182345320/article/details/128105718