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鲁棒局部均值分解 (RLMD)(Matlab代码实现)

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📋📋📋本文目录如下:🎁🎁🎁

目录

​​💥1 概述​​

​​📚2 运行结果​​

​​🎉3 参考文献​​

​​🌈4 Matlab代码实现​​

💥1 概述

RLMD 是一种改进的局部均值分解,由一组优化策略提供支持。优化策略可以处理LMD中的边界条件、包络估计和筛选停止准则。它同时从混合信号中提取一组单分量信号(称为乘积函数)及其相关的解调信号(即AM信号和FM信号),与其他自适应信号处理方法(如EMD)相比,这是最吸引人的特征。RLMD可用于时频分析。

📚2 运行结果

鲁棒局部均值分解 (RLMD)(Matlab代码实现)
鲁棒局部均值分解 (RLMD)(Matlab代码实现)
鲁棒局部均值分解 (RLMD)(Matlab代码实现)
鲁棒局部均值分解 (RLMD)(Matlab代码实现)

部分代码:

[x, display, stop_thre, sifting_stopping_mode, max_iter, max_pfs, smooth_mode,...

    ma_span, ma_iter_mode, extd_r, x_energy, pfs, ams, fms, iterNum, fvs]...

    = initial(x,varargin{:});

% Initialize main loop

i = 0;

xs = x; % copy x to xs for sifting process, reserve original input as x.

nx = length(x);

while i < max_pfs && ~stoplmd(xs, x_energy) % outer loop for PF selection    

    i = i+1;    

    % initialize variables used in PF sifting loop

    a_i = ones(1,nx);

    s_j = zeros(max_iter,nx);

    a_ij = zeros(max_iter, nx);

    % PF sifting iteration loop

    j = 0;

    stop_sifting = 0;

    s = xs;

    while j < max_iter && ~stop_sifting %  inner loop for sifting process

        j = j+1;

        [m_j, a_j, n_extr] = lmd_mean_amp(s, smooth_mode, ma_span, ma_iter_mode,...

            extd_r);

        % force to stop iter if number of extrema of s is smaller than 3.

        if n_extr < 3

            break;

        end

        h_j = s-m_j; % remove mean.

        s = h_j./a_j; % demodulate amplitude.

        a_i = a_i .* a_j; % mulitiply every ai

        a_ij(j, :) = a_i;

        s_j(j, :) = s;

        [stop_sifting,fvs(i,:)] = is_sifting_stopping(a_j, j, fvs(i,:), sifting_stopping_mode, stop_thre);

    end % sift iteration loop

    switch sifting_stopping_mode

        case {'liu'}

            [~, opt0] = min(fvs(i,1:j)); % ***Critical Step***

            opt_IterNum = min(j, opt0); % in case iteration stop for n_extr<3

            %             opt_IterNum = min(j-2, opt0);

        otherwise

            error('No specifications for sifting_stopping_mode.');

    end

    ams(i, :) = a_ij(opt_IterNum, :); % save each amplitude modulation function in ams.

    fms(i, :) = s_j(opt_IterNum, :); % save each pure frequency modulation function in fms.

    pfs(i, :) = ams(i, :).*fms(i, :); % gain Product Funcion.

    xs = xs-pfs(i, :); % remove PF just obtained from input signal;

    iterNum(i) = opt_IterNum; % record the iteration times taken by of each PF sifing.

end % main loop

pfs(i+1, :) = xs; % save residual in the last row of PFs matrix.

ams(i+1:end,:) = []; fms(i+1:end,:) = []; pfs(i+2:end,:) = []; fvs(i+1:end,:) = [];

ort = io(x, pfs);

% Output visualization

if display == 1

    lmdplot(pfs, ams, fms, smooth_mode);

end

end

%--------------------------- built-in functions ---------------------------

% initialize signal and options

function [x, display, stop_thre, sifting_stopping_mode, max_iter, max_pfs, smooth_mode,...

    ma_span, ma_iter_mode, extd_r, x_energy, pfs, ams, fms, iterNum, fvs]...

    = initial(x,varargin)

🎉3 参考文献

​​🌈​​4 Matlab代码实现

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