时序分解 | Matlab实现EEMD集合经验模态分解时间序列信号分解

时序分解 | Matlab实现EEMD集合经验模态分解时间序列信号分解

效果一览

基本介绍

Matlab实现EEMD集合经验模态分解时间序列信号分解
1.分解效果图 ，效果如图所示，可完全满足您的需求～
2.直接替换数据即可用 适合新手小白 注释清晰～
3.附赠案例数据 直接运行main一键出图～

程序设计

• 完整源码和数据获取方式：Matlab实现EEMD集合经验模态分解时间序列信号分解。

function [modos its]=eemd(x,Nstd,NR,MaxIter)
%--------------------------------------------------------------------------
%WARNING: this code needs to include in the same
%directoy the file emd.m developed by Rilling and Flandrin.
% -------------------------------------------------------------------------
%  OUTPUT
%   modos: contain the obtained modes in a matrix with the rows being the modes
%   its: contain the iterations needed for each mode for each realization
%
%  INPUT
%  x: signal to decompose
%  Nstd: noise standard deviation
%  NR: number of realizations
%  MaxIter: maximum number of sifting iterations allowed.
% -------------------------------------------------------------------------
%   Syntax
%
%   modos=eemd(x,Nstd,NR,MaxIter)
%  [modos its]=eemd(x,Nstd,NR,MaxIter)
% -------------------------------------------------------------------------
%  NOTE:   if Nstd=0 and NR=1, the EMD decomposition is obtained.
% -------------------------------------------------------------------------
 

iter=it;
if NR>=2
    for i=2:NR
        xconruido=x+Nstd*randn(size(x));
        [temp, ort, it]=emd(xconruido,'MAXITERATIONS',MaxIter);
        temp=temp/NR;
        lit=length(it);
        [p liter]=size(iter);
        if lit<liter
            it=[it zeros(1,liter-lit)];
        end;
        if liter<lit
            iter=[iter zeros(p,lit-liter)];
        end;
        
        iter=[iter;it];
        
        [filas columnas]=size(temp);
        [alto ancho]=size(modos);
        diferencia=alto-filas;
        if filas>alto
            modos=[modos; zeros(abs(diferencia),ancho)];
        end;
        if alto>filas
            temp=[temp;zeros(abs(diferencia),ancho)];
        end;
        
        modos=modos+temp;
    end;
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参考资料

[1] https://blog.csdn.net/kjm13182345320/article/details/129215161
[2] https://blog.csdn.net/kjm13182345320/article/details/128105718