EEMD算法应用于信号去噪.rar

function allmode=eemd(Y,Nstd,NE) % This is an EMD/EEMD program % % 输入： % Y，Inputted data：1-d data only % Nstd，附加噪声的标准差与Y的标准差之比：Nstd = (0.1 ~ 0.4)*std(Y). % NE，EEMD中添加噪声的总体次数：NE = 10-50. % % 输出： % N*（m+1）的矩阵，其中N是输入数据Y的长度，m=fix(log2(N))-1. % 输出的矩阵中，第一列为原始输入数据，第2,3，...，m列是从高频到低频的IMF分量，m+1列是残余分量。 % % 注意： % 当Nstd设置为0，NE设置为1时，该EEMD程序就退化为EMD程序。 % 此代码限制筛选数=10，停止标准不能更改。 % % 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 % % 这段代码中有三个循环耦合在一起。 % 1.读取数据，找出标准差，用标准差对所有数据进行分离 % 2.将TNM评估为IMF总数--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.添加噪声 % 5.筛选前给出初始值 % 6.开始寻找IMF------IMF loop start % 7.筛选10次以获得IMF------sift loop start and end % 8.10次筛后 --we got IMF % 9.从数据中减去IMF，让残差通过循环找到下一个IMF % 6.在拥有了所有的imf之后-------------IMF loop end % 9.经TNM-IMFs处理后，残差呈总体趋势 % 3.总结NE分解结果--------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))-5; % TNM=m 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; % 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 0to 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; nmode=nmode+1; %part9--after sift 10 times,that xstart is this time IMF for jj=1:1:xsize mode(jj,nmode) = xstart(jj); end end %part10--after gotten all(TNM) IMFs ,the residual xend is over all trend % put them in the last column for jj=1:1:xsize mode(jj,nmode+1)=xend(jj); end %after part 10 ,original + TNM IMFs+overall trend ---those are all in mode allmode=allmode+mode; end %part3 Do EEMD -----EEMD loop end %part11--devide EEMD summation by NE,std be multiply back to data allmode=allmode/NE; allmode=allmode*Ystd;