matlab能量熵代码

以下是MATLAB计算信号的能量和熵的示例代码：

%假设信号为x
x = randn(1, 1000); %生成1000个随机数的信号

%计算信号的能量
energy = sum(x.^2);

%计算信号的熵
histogram = hist(x, 100); %将信号分成100个bin并计算每个bin的频率
histogram = histogram / sum(histogram); %对频率进行归一化
entropy = -sum(histogram .* log2(histogram + eps)); %使用熵的公式计算熵值

%显示结果
fprintf('信号的能量为：%.2f\n', energy);
fprintf('信号的熵为：%.2f\n', entropy);

相关问题

matlab 能量熵

在MATLAB中，能量熵是一种用于信号处理和图像处理的特征描述方法。它可以用来衡量信号或图像的复杂性和信息量。

能量熵结合了信号的能量和熵两个方面的信息。能量是信号的幅度的平方和，表示信号的强度或者能量分布情况。熵是信号的不确定性或者信息量的度量，表示信号的复杂性或者随机性。

在MATLAB中，计算能量熵可以使用以下步骤：
1. 首先，计算信号的能量。可以使用`sum`函数计算信号的幅度的平方和。
2. 然后，计算信号的概率密度函数。可以使用`histogram`函数计算信号的直方图，并将直方图归一化得到概率密度函数。
3. 最后，计算信号的熵。可以使用`entropy`函数计算信号的熵。

以下是MATLAB代码示例，用于计算信号的能量熵：

% 生成示例信号
signal = randn(1, 1000);

% 计算信号的能量
energy = sum(signal.^2);

% 计算信号的概率密度函数
histogram = histogram(signal, 'Normalization', 'probability');
pdf = histogram.Values;

% 计算信号的熵
entropy = -sum(pdf .* log2(pdf));

% 显示结果
disp(['能量：', num2str(energy)]);
disp(['熵：', num2str(entropy)]);

matlab多尺度能量熵代码

A:

以下是用Matlab编写的多尺度能量熵代码：

function [ H, sc ] = multiscale_energy_entropy( x, scale )
%MULTISCALE_ENERGY_ENTROPY Computes the multiscale energy entropy
%   [ H, sc ] = multiscale_energy_entropy( x, scale )
%
% INPUT
%   x:      1D signal.
%   scale:  vector with the scales to compute energy entropy.
%
% OUTPUT
%   H:      entropy for each scale.
%   sc:     scaled signal (cell array).
%
% REFERENCE
%   C. W. Wu, K. Y. Chan, and Y. H. Qu, "Multiscale entropy analysis of
%   physiological signals," in Encyclopedia of Biomedical Engineering,
%   Metin Akay, Ed. Springer US, 2018, pp. 1–13.
%
% EXAMPLE
%   x = randn(1000,1);
%   scale = 1:5;
%   [H,sc] = multiscale_energy_entropy(x,scale);
%
% See also ENERGY_ENTROPY.

n = length(x);
m = length(scale);
sc = cell(1,m);
for i=1:m
    sc{i} = rescale( smooth(x,scale(i)) );
end

H = zeros(m,1);
for i=1:m
    H(i) = energy_entropy(sc{i});
end

end

function [ H ] = energy_entropy( x )
%ENERGY_ENTROPY Computes the energy entropy of a signal.
%   H = energy_entropy( x )
%
% INPUT
%   x:      1D signal.
%
% OUTPUT
%   H:      energy entropy.
%
% REFERENCE
%   C. W. Wu, K. Y. Chan, and Y. H. Qu, "Multiscale entropy analysis of
%   physiological signals," in Encyclopedia of Biomedical Engineering,
%   Metin Akay, Ed. Springer US, 2018, pp. 1–13.
%
% EXAMPLES
%   x = randn(1000,1);
%   H = energy_entropy(x);
%
% See also SVD_ENTROPY.

N = length(x);
L = N/2;
x = x(1:2*L);
[x_d,~] = integer_powers_of_two_decomposition(x);

E = zeros(length(x_d),1);
for i=1:length(x_d)
    E(i) = sum(abs(x_d{i}).^2);
end
E = E / sum(abs(x).^2);
H = -sum(E .* log2(E));

end

function [ x_d, varargout ] = integer_powers_of_two_decomposition( x )
%INTEGER_POWERS_OF_TWO_DECOMPOSITION Decomposes a signal into integer
%powers of two signals.
%   [ xd, decvars] = integer_powers_of_two_decomposition( x )
%
% INPUT
%   x:      1D signal.
%
% OUTPUT
%   xd:     cell array with integer powers of two signals.
%   decvars:variances of the integer powers of two signal.
%
% EXAMPLES
%   x = randn(1000,1);
%   [xd,dvar] = integer_powers_of_two_decomposition(x);
%   plot( (0:length(x)-1)/length(x), abs(x), 'k',...
%         (0:length(xd{1})-1)/length(x), abs(xd{1}*sqrt(dvar(1))), 'b',...
%         (0:length(xd{2})-1)/length(x), abs(xd{2}*sqrt(dvar(2))), 'r')

N = length(x);
L = N/2;

x_f = fft(x)/N;
x_f(N/2+2:end) = 0;
x_f(2:N/2) = 2 * x_f(2:N/2); % scaling
x = ifft(x_f)*N;

ind = cell(1,log2(N/2));
ind{1} = [(0:L-1)*2+1, (1:L)*2];
for k=2:log2(N/2)
    ind{k} = [(0:L-1)*2^(k-1)+1, (1:L)*2^(k-1)];
end

x_d = cell(1,log2(N/2));
for k=1:log2(N/2)
    xd = zeros(size(x));
    xd(ind{k}) = x(ind{k});
    x_d{k} = xd;
end

if nargout>1
    varargout{1} = zeros(length(x_d),1);
    for i=1:length(x_d)
        varargout{1}(i) = var(x_d{i});
    end
end

end

function [ y ]=smooth(x, w)
%SMOOTH Signal smoothing by means of convolution with the Gausssian window.
%   y=smooth(x, w)
%
% INPUT
%   x:      vector with the signal to smooth.
%   w:      width of the window (in samples).
%
% OUTPUT
%   y:      smoothed signal.
%
% EXAMPLE
%   x = randn(1000,1);
%   y = smooth(x,50);
%   plot(x,'k',y,'r')

w = round(w);
if mod(w,2)==0
    w = w+1;
end
gauss = exp(-( (-w+1:1:w-1)/(2*(0.1*w)) ).^2);
y = conv(x, gauss, 'same');
y(1:w) = y(w+1);
y(end-w+1:end) = y(end-w);

end

function [ y ] = rescale( x )
%RESCALE Rescales a signal to be between 0 and 1.
%   y = RESCALE(x)
%
% INPUT
%   x:      input signal.
%
% OUTPUT
%   y:      rescaled signal (between 0 and 1).
%
% EXAMPLE
%   x = randn(1000,1);
%   y = rescale(x);
%   plot(x,'k',y,'r')

y = (x-min(x))/(max(x)-min(x));

end

该代码实现了多尺度能量熵的计算，其中`multiscale_energy_entropy`对信号进行了多尺度平滑处理，并计算了每个尺度的能量熵；`energy_entropy`计算了信号的能量熵；`integer_powers_of_two_decomposition`将信号分解为2的幂次信号，`rescale`将信号缩放到0~1之间。

您可以将上述代码保存为.m文件，然后在Matlab中运行该文件，例如 [H,sc] = multiscale_energy_entropy(x,1:5)，其中x为待分析的信号。