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
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