完全集合经验模态分解的matlab代码

完全集合经验模态分解（EEMD）的Matlab代码如下：

function allmode = eemd(Y, Nstd, NE)
    xsize = length(Y);
    dd = 1:1:xsize;
    Ystd = std(Y);
    Y = Y / Ystd;
    TNM = fix(log2(xsize)) - 1;
    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
        for i = 1:xsize
            temp = randn(1, 1) * Nstd;
            X1(i) = Y(i) + temp;
        end

        for jj = 1:1:xsize
            mode(jj, 1) = Y(jj);
        end

        xorigin = X1;
        xend = xorigin;
        nmode = 1;

        while nmode <= TNM
            xstart = xend;
            iter = 1;

            while iter <= 10
                [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;

            for jj = 1:1:xsize
                mode(jj, nmode) = xstart(jj);
            end
        end

        for jj = 1:1:xsize
            mode(jj, nmode + 1) = xend(jj);
        end

        allmode = allmode + mode;
    end

    allmode = allmode / NE;
    allmode = allmode * Ystd;
end

这段代码实现了完全集合经验模态分解（EEMD），其中`Y`是输入信号，`Nstd`是附加的白噪声的标准差，`NE`是进行EEMD的次数。代码首先对输入信号进行标准化处理，然后根据指定的噪声标准差和EEMD次数进行循环操作。在每次循环中，会生成一个包含附加噪声的新信号，并使用改进的EMD方法进行模态分解。最后，将所有的模态分量累加得到最终的结果。请注意，为了运行这段代码，你需要在Matlab环境中安装相关的工具包和函数（如`spline`和`extrema`）。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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