pod分解matlab程序

function [phi, lam, Xmean, nbasis]=POD(X,cenergy) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % This function calculates the POD basis vectors % % Function inputs: % X : (n x nsnap) snapshot matrix % n = number of states in full space % nsnap = number of snapshots % cenergy : how much energy of the ensemble you want % to capture, i.e. cenergy = 99.9 (percent) % % Function outputs: % phi : (n x nsnap) matrix containing POD basis vectors % lam : (nsnap x 1) vector containing POD eigenvalues % Xmean : (n x 1) the mean of the ensemble X % nbasis : number of POD basis vectors you should use % to capture cenergy (percent) of the ensemble %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % calculate the mean of the snapshots nsnap = size(X,2); Xmean = sum(X,2)/nsnap; % Obtain new snapshot ensemble with zero mean for i=1:nsnap X1(:,i) = X(:,i)-Xmean; end % METHOD OF SNAPSHOTS % calculate the empirical correlation matrix C C = X1'*X1/nsnap; % Calculate the POD basis [evectorC,evalueC] = eig(C); phi = X1 * evectorC; % Normalize the POD basis for i=1:nsnap phi(:,i) = phi(:,i)/norm(phi(:,i),2); end % return the POD eigenvalues in a vector lam = diag(evalueC); % Rearrange POD eigenvalues, vectors in descending order. % Note that the correlation matrix C is symmetric, so SVD and EIG % will give the same evectorC and evalueC but they are already in % descending order and hence we don't need to rearrange evectorC and evalueC % if SVD is used lam = rot90(lam,2); phi = fliplr(phi); %%% Find the number of POD basis vectors capturing cenergy (percent) of energy %%%% % total energy tenergy = sum(lam); energy = 0.; nbasis = 0; i = 1; while (((energy/tenergy)*100) < cenergy) energy = energy + lam(i); i = i+1; end nbasis = i; % plot eigenvalues corresponding to nbasis vectors plot(lam(1:nbasis)/tenergy/100,'*') xlabel('Number of POD eigenvalues') ylabel('Energy captured')