时序分解 | Matlab实现NGO-ICEEMDAN基于北方苍鹰算法优化ICEEMDAN时间序列信号分解

效果一览

基本介绍

Matlab实现NGO-ICEEMDAN基于北方苍鹰算法优化ICEEMDAN时间序列信号分解
1.ICEEMDAN方法的分解效果取决于白噪声幅值权重(Nstd)和噪声添加次数(NE)，因此，采用智能优化算法对这2个参数进行优化，适应度函数包括包络熵、样本熵、信息熵、排列熵。
2.直接替换Excel数据即可用，注释清晰，适合新手小白[火]
3.附赠时间序列测试数据，可直接运行main一键出图[闪亮]

程序设计

• 完整源码和数据获取方式私信博主回复：Matlab实现NGO-ICEEMDAN基于北方苍鹰算法优化ICEEMDAN时间序列信号分解。

[x, y] = size(signal);
if x > y
	C = y;% number of channels
    T = x;% length of the Signal
	signal = signal';
else
	C = x;% number of channels
    T = y;% length of the Signal
end
%---------- Preparations
% Sampling Frequency
fs = 1/T;

% Mirroring
f(:,1:T/2) = signal(:,T/2:-1:1);
f(:,T/2+1:3*T/2) = signal;
f(:,3*T/2+1:2*T) = signal(:,T:-1:T/2+1);
% Time Domain 0 to T (of mirrored signal)
T = size(f,2);
t = (1:T)/T;
% frequencies
freqs = t-0.5-1/T;
% Construct and center f_hat
f_hat = fftshift(fft(f,[],2),2);
f_hat_plus = f_hat;
f_hat_plus(:,1:T/2) = 0;

%------------ Initialization
% Maximum number of iterations 
N = 500;
% For future generalizations: individual alpha for each mode
Alpha = alpha*ones(1,K);
% matrix keeping track of every iterant 
u_hat_plus_00 = zeros(length(freqs), C, K);
u_hat_plus = zeros(length(freqs), C, K);
omega_plus = zeros(N, K);
% initialize omegas uniformly
switch init
	case 1
        omega_plus(1,:) = (0.5/K)*((1:K)-1);
    case 2
        omega_plus(1,:) = sort(exp(log(fs) + (log(0.5)-log(fs))*rand(1,K)));
    otherwise
        omega_plus(1,:) = 0;
end

参考资料

[1] https://blog.csdn.net/kjm13182345320/article/details/129215161
[2] https://blog.csdn.net/kjm13182345320/article/details/128105718