代码

main1.m文件

clc
clear all

load IMF1
load IMF2
load IMF3
load IMF4

a2=IMF1;
b2=IMF2;
c2=IMF3;
d2=IMF4;

a=a2;
fe=zeros(1,4);
a=a(randperm(length(a),200));
fe_a2=FuzzyEntropy(a',2,0.15,2,1);
fe(:,1)=fe_a2;

a=b2;
a=a(randperm(length(a),200));
fe_b2=FuzzyEntropy(a',2,0.15,2,1);
fe(:,2)=fe_b2;

a=c2;
a=a(randperm(length(a),200));
fe_c2=FuzzyEntropy(a',2,0.15,2,1);
fe(:,3)=fe_c2;

a=d2;
a=a(randperm(length(a),200));
fe_d2=FuzzyEntropy(a',2,0.15,2,1);
fe(:,4)=fe_d2;

plot(fe,'-o r');
ylabel('FE');
xlabel('分解层数');
set(gca,'xtick',[1:1:4]);

FuzzyEntropy.m文件

%% 模糊熵函数
function FuzEn = FuzzyEntropy(data,dim,r,n,tau)
%
% This function calculates fuzzy entropy (FuzEn) of a univariate signal data
%
% Inputs:
%
% data: univariate signal - a vector of size 1 x N (the number of sample points)
% dim: embedding dimension
% r: threshold (it is usually equal to 0.15 of the standard deviation of a signal - because we normalize signals to have a standard deviation of 1, here, r is usually equal to 0.15)
% n: fuzzy power (it is usually equal to 2)
% tau: time lag (it is usually equal to 1)
% 模糊熵算法的提出者：Chen Weiting,Wang Zhizhong,XieHongbo,et al. Characterization of surfaceEMG signal based on fuzzy entropy. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2007,15(2):266-272.
%
if nargin == 4, tau = 1; end
if nargin == 3, n = 2; tau=1; end
if tau > 1, data = downsample(data, tau); end
 
N = length(data);
result = zeros(1,2);
 
for m = dim:dim+1
    count = zeros(N-m+1,1);
    dataMat = zeros(N-m+1,m);
    
    % 设置数据矩阵，构造成m维的矢量
    for i = 1:N-m+1
        dataMat(i,:) = data(1,i:i+m-1);
    end
    % 利用距离计算相似模式数
    for j = 1:N-m+1
        % 计算切比雪夫距离，不包括自匹配情况
        dataMat=dataMat-mean(dataMat,2);
        tempmat=repmat(dataMat(j,:),N-m+1,1);
        dist = max(abs(dataMat - tempmat),[],2);
        D=exp(-(dist.^n)/r);
        count(j) = (sum(D)-1)/(N-m);
    end
    result(m-dim+1) = sum(count)/(N-m+1);
end
    % 计算得到的模糊熵值
    FuzEn = log(result(1)/result(2));
end

数据

结果

如有需要代码和数据的同学请在评论区发邮箱，一般一天之内会回复，请点赞＋关注谢谢！！