EMD分解语音信号求近似熵
哪位大神会近似熵的程序啊，对已经分解的imf分量（EMD分解）求近似熵，求好心人帮帮忙，有酬谢哦... 百度搜一下很多程序http://chainaren2008.blog.163.com/blog/static/17814101920113383349577/ 这个程序我试过，有错，调试过也不行，而且程序不全，后面应该还有几句 zhendongbang 发表于 2014-5-4 10:38这个程序我试过，有错，调试过也不行，而且程序不全，后面应该还有几句
function = approx_entropy(n,r,a)
%% Code for computing approximate entropy for a time series: Approximate
% Entropy is a measure of complexity. It quantifies the unpredictability of
% fluctuations in a time series
% To run this function- type: approx_entropy('window length','similarity measure','data set')
% i.eapprox_entropy(5,0.5,a)
% window length= length of the window, which should be considered in each iteration
% similarity measure = measure of distance between the elements
% data set = data vector
% small values of apen (approx entropy) means data is predictable, whereas
% higher values mean that data is unpredictable
% concept boorowed from http://www.physionet.org/physiotools/ApEn/
% Author: Avinash Parnandi
%%
data =a;
for m=n:n+1; % run it twice, with window size differing by 1
set = 0;
count = 0;
counter = 0;
window_correlation = zeros(1,(length(data)-m+1));
for i=1:(length(data))-m+1,
current_window = data(i:i+m-1); % current window stores the sequence to be compared with other sequences
for j=1:length(data)-m+1,
sliding_window = data(j:j+m-1); % get a window for comparision with the current_window
% compare two windows, element by element
% can also use some kind of norm measure; that will perform better
for k=1:m,
if((abs(current_window(k)-sliding_window(k))>r) && set == 0)
set = 1; % i.e. the difference between the two sequence is greater than the given value
end
end
if(set==0)
count = count+1; % this measures how many sliding_windows are similar to the current_window
end
set = 0; % reseting 'set'
end
counter(i)=count/(length(data)-m+1); % we need the number of similar windows for every cuurent_window
count=0;
i;
end%for i=1:(length(data))-m+1, ends here
counter;% this tells how many similar windows are present for each window of length m
%total_similar_windows = sum(counter);
%window_correlation = counter/(length(data)-m+1);
correlation(m-n+1) = ((sum(counter))/(length(data)-m+1));
end % for m=n:n+1; % run it twice
correlation(1);
correlation(2);
apen = log(correlation(1)/correlation(2));
没用过近似商，这个是别人写的一个程序，你试试吧，
等待验证中.... 我也在等待
页:
[1]