为了抑制经验模态分解中出现的端点效应和模态混叠现象,利用白噪声辅助数据分析方法——集合经验模态分解构造一个自适应滤波器组,对原信号进行各级滤波,最终得到纯净的信号.然后与小波阈值去噪方法进行比较,通过仿真可以看出,集合经验模态分解构造的滤波器组滤波效果比较理想.
% 这是一个用于显著性检验的实用程序.
%
% function [spmax, spmin, flag]= extrema(in_data)
%
% INPUT:
% in_data: Inputted data, a time series to be sifted(被筛选);
% OUTPUT:
% spmax: The locations (col 1) of the maxima and its corresponding
% values (col 2)
% spmin: The locations (col 1) of the minima and its corresponding
% values (col 2)
%
% References can be found in the "Reference" section.
%
% The code is prepared by Zhaohua Wu. For questions, please read the "Q&A" section or
% contact
% [email protected]
%
function [spmax, spmin, flag]= extrema(in_data)
flag=1;
dsize=length(in_data);
spmax(1,1) = 1;
spmax(1,2) = in_data(1);
jj=2;
kk=2;
while jj
spmax(kk,1) = jj;
spmax(kk,2) = in_data (jj);
kk = kk+1;
end
jj=jj+1;
end
spmax(kk,1)=dsize;
spmax(kk,2)=in_data(dsize);
if kk>=4
slope1=(spmax(2,2)-spmax(3,2))/(spmax(2,1)-spmax(3,1));
tmp1=slope1*(spmax(1,1)-spmax(2,1))+spmax(2,2);
if tmp1>spmax(1,2)
spmax(1,2)=tmp1;
end
slope2=(spmax(kk-1,2)-spmax(kk-2,2))/(spmax(kk-1,1)-spmax(kk-2,1));
tmp2=slope2*(spmax(kk,1)-spmax(kk-1,1))+spmax(kk-1,2);
if tmp2>spmax(kk,2)
spmax(kk,2)=tmp2;
end
else
flag=-1;
end
msize=size(in_data);
dsize=max(msize);
xsize=dsize/3;
xsize2=2*xsize;
spmin(1,1) = 1;
spmin(1,2) = in_data(1);
jj=2;
kk=2;
while jj
spmin(kk,1) = jj;
spmin(kk,2) = in_data (jj);
kk = kk+1;
end
jj=jj+1;
end
spmin(kk,1)=dsize;
spmin(kk,2)=in_data(dsize);
if kk>=4
slope1=(spmin(2,2)-spmin(3,2))/(spmin(2,1)-spmin(3,1));
tmp1=slope1*(spmin(1,1)-spmin(2,1))+spmin(2,2);
if tmp1
end
slope2=(spmin(kk-1,2)-spmin(kk-2,2))/(spmin(kk-1,1)-spmin(kk-2,1));
tmp2=slope2*(spmin(kk,1)-spmin(kk-1,1))+spmin(kk-1,2);
if tmp2
end
else
flag=-1;
end
flag=1;
[1]周先春, and 嵇亚婷. "基于EEMD算法在信号去噪中的应用." 电子设计工程 22.8(2014):3.
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