【变分模态分解】基于matlab VMD变分模态分解（含信号图 分解效果图 频谱图）【含Matlab源码 7542期】.zip

function [u, u_hat, omega] = VMD(signal, alpha, tau, K, DC, init, tol) % Input and Parameters: % --------------------- % Input Parameters: % signal：要分解的时域信号 % alpha： 惩罚因子，也称平衡参数 % tau：噪声容忍度 % K：分解的模态数 % DC：直流分量 % init：初始化中心频率 % 0 = all omegas start at 0 % 1 = all omegas start uniformly distributed % 2 = all omegas initialized randomly % tol：收敛准则容忍度；通常在1e-6左右。 % % Output Parameters: % u：分解模式的集合 % u_hat：模式的频谱 % omega：估计模式中心频率 %---------- Preparations % 输入信号的周期和采样频率 save_T = length(signal); fs = 1/save_T; % 通过镜像扩展信号 T = save_T; f_mirror(1:T/2) = signal(T/2:-1:1); f_mirror(T/2+1:3*T/2) = signal; f_mirror(3*T/2+1:2*T) = signal(T:-1:T/2+1); f = f_mirror; % 时域0到T(镜像信号) T = length(f); t = (1:T)/T; % Spectral Domain discretization freqs = t-0.5-1/T; % 最大迭代数 N = 500; % For future generalizations: individual alpha for each mode Alpha = alpha*ones(1,K); % Construct and center f_hat f_hat = fftshift((fft(f))); f_hat_plus = f_hat; f_hat_plus(1:T/2) = 0; % matrix keeping track of every iterant // could be discarded for mem u_hat_plus = zeros(N, length(freqs), K); % Initialization of omega_k omega_plus = zeros(N, K); switch init case 1 for i = 1:K omega_plus(1,i) = (0.5/K)*(i-1); end case 2 omega_plus(1,:) = sort(exp(log(fs) + (log(0.5)-log(fs))*rand(1,K))); otherwise omega_plus(1,:) = 0; end % if DC mode imposed, set its omega to 0 if DC omega_plus(1,1) = 0; end % start with empty dual variables lambda_hat = zeros(N, length(freqs)); % other inits uDiff = tol+eps; % update step n = 1; % loop counter sum_uk = 0; % accumulator % ----------- Main loop for iterative updates while ( uDiff > tol && n < N ) % not converged and below iterations limit % update first mode accumulator k = 1; sum_uk = u_hat_plus(n,:,K) + sum_uk - u_hat_plus(n,:,1); % update spectrum of first mode through Wiener filter of residuals u_hat_plus(n+1,:,k) = (f_hat_plus - sum_uk - lambda_hat(n,:)/2)./(1+Alpha(1,k)*(freqs - omega_plus(n,k)).^2); % update first omega if not held at 0 if ~DC omega_plus(n+1,k) = (freqs(T/2+1:T)*(abs(u_hat_plus(n+1, T/2+1:T, k)).^2)')/sum(abs(u_hat_plus(n+1,T/2+1:T,k)).^2); end % update of any other mode for k=2:K % accumulator sum_uk = u_hat_plus(n+1,:,k-1) + sum_uk - u_hat_plus(n,:,k); % mode spectrum u_hat_plus(n+1,:,k) = (f_hat_plus - sum_uk - lambda_hat(n,:)/2)./(1+Alpha(1,k)*(freqs - omega_plus(n,k)).^2); % center frequencies omega_plus(n+1,k) = (freqs(T/2+1:T)*(abs(u_hat_plus(n+1, T/2+1:T, k)).^2)')/sum(abs(u_hat_plus(n+1,T/2+1:T,k)).^2); end % Dual ascent lambda_hat(n+1,:) = lambda_hat(n,:) + tau*(sum(u_hat_plus(n+1,:,:),3) - f_hat_plus); % loop counter n = n+1; % converged yet? uDiff = eps; for i=1:K uDiff = uDiff + 1/T*(u_hat_plus(n,:,i)-u_hat_plus(n-1,:,i))*conj((u_hat_plus(n,:,i)-u_hat_plus(n-1,:,i)))'; end uDiff = abs(uDiff); end %------ Postprocessing and cleanup % discard empty space if converged early N = min(N,n); omega = omega_plus(1:N,:); % Signal reconstruction u_hat = zeros(T, K); u_hat((T/2+1):T,:) = squeeze(u_hat_plus(N,(T/2+1):T,:)); u_hat((T/2+1):-1:2,:) = squeeze(conj(u_hat_plus(N,(T/2+1):T,:))); u_hat(1,:) = conj(u_hat(end,:)); u = zeros(K,length(t)); for k = 1:K u(k,:)=real(ifft(ifftshift(u_hat(:,k)))); end % remove mirror part u = u(:,T/4+1:3*T/4); % recompute spectrum clear u_hat; for k = 1:K u_hat(:,k)=fftshift(fft(u(k,:)))'; end end