预测模型基于matlab粒子群算法预测【含Matlab源码 1326期】.zip

clear all; clc; close all; % ¶¨ÒåÊý¾Ý²úÉúʽ x_string = '-2*x(1) + 3*x(2).^2 - 1*x(3) + 4*x(4).^2 + 10*x(5) + 6*x(6) + 50'; % ÌáÈ¡xdataºÍydata xdata_all = rand(200, 6); ydata_all = []; for i = 1:size(xdata_all,1) x = xdata_all(i,:); y = eval(x_string); ydata_all(i,1) = y; end % xdata = xdata_all(1:158,:); ydata = ydata_all(1:158); xdata_test = xdata_all(159:end,:); ydata_test = ydata_all(159:end); % ¶¨ÒåÁ£×ÓËã·¨²ÎÊý N = 50; % Á£×ÓÖÖȺÊýÁ¿ d = size(xdata,2)*2 + 1; % ¶¨Òåÿ¸öÁ£×ӵij¤¶È ger = 100; % ¶¨Òå×î´óµü´ú´ÎÊý % ¶¨ÒåλÖòÎÊýÉèÖà limit_sub = zeros(1,d); limit_up = zeros(1,d); for i = 1:d if i ~= d if mod(i,2) == 1 limit_sub(i) = -30; limit_up(i) = 30; else limit_sub(i) = -30; limit_up(i) = 30; end else limit_sub(i) = -100; limit_up(i) = 100; end end % ¶¨ÒåËÙ¶ÈÏÞÖÆ vlimit = [-30, 30]; % ¶¨Òå¹ßÐÔȨÖØ Wmax = 0.8; Wmin = 0.2; % ¶¨Òå×ÔÎÒѧϰÒò×Ó c1 = 0.5; % ¶¨ÒåȺÌåѧϰÒò×Ó c2 = 0.5; % ³õʼ»¯ÈºÌå XX = zeros(N,d); for i = 1:N XX(i,:) = limit_sub + (limit_up - limit_sub).*rand(1,d); end % ³õʼ»¯ÖÖȺËÙ¶È V = rand(N,d); Xm = XX; % ÿ¸öÁ£×Ó¸öÌåµÄÀúÊ·×î¼ÑλÖà fXm = fit_fun(XX, xdata, ydata); % ÿ¸ö¸öÌåµÄÀúÊ·×î¼ÑÊÊÓ¦¶È [fYm, nmin] = min(fXm); % ÖÖȺÀúÊ·×î¼ÑÊÊÓ¦¶È Ym = Xm(nmin,:); % ÖÖȺµÄÀúÊ·×î¼ÑÊÊÓ¦¶È % ȺÌå¸üРiter = 1; record = zeros(ger,1); while iter <= ger fx = fit_fun(XX, xdata, ydata); % ¸öÌ嵱ǰÊÊÓ¦¶È for i = 1:N if fXm(i) > fx(i) fXm(i) = fx(i); % ¸üиöÌåÀúÊ·×î¼ÑÊÊÓ¦¶È Xm(i,:) = XX(i,:); % ¸üиöÌåÀúÊ·×î¼ÑλÖà end end if fYm > min(fXm) [fYm, nmin] = min(fXm); % ¸üÐÂȺÌåÀúÊ·×î¼ÑÊÊÓ¦¶È Ym = Xm(nmin,:); % ¸üÐÂȺÌåÀúÊ·×î¼ÑλÖà end V = V * (Wmin + (Wmax - Wmin)*(ger - iter)/ger) + c1*rand(N,d).*(Xm - XX) + c2*rand(N,d).*(repmat(Ym,N,1) - XX); % ËٶȸüР% ±ß½çËٶȴ¦Àí V(V > vlimit(2)) = vlimit(2); V(V < vlimit(1)) = vlimit(1); % ½øÐÐλÖøüРXX = XX + V; % Ôö¼ÓÒ»¸öËæ»ú½»»»µÄ²Ù×÷ for i = 1:N if rand < 0.3 X_sub = XX(i,:); K = randperm(d,2); X_sub([K(1), K(2)]) = X_sub([K(2), K(1)]); XX(i,:) = X_sub; end end % ±ß½ç´¦Àí for i = 1:N x_sub = XX(i,:); for j = 1:d if x_sub(j) < limit_sub(j) x_sub(j) = limit_sub(j); end if x_sub(j) > limit_up(j) x_sub(j) = limit_up(j); end end XX(i,:) = x_sub; end record(iter) = fYm; iter = iter + 1; fprintf('%d\t%f\n', iter - 1, fYm); end % »æÖÆÊÕÁ²ÇúÏßͼ figure; plot(record); title('����ͼ'); print(gcf, '-dpng', 'fig1.png'); % »ñµÃ×î¼ÑµÄ»Ø¹é·½³Ìʽ [~, Xstring] = fit_fun(Ym, xdata, ydata); Xstring = Xstring{1}; % ¸ù¾Ý»Ø¹éʽ¶Ô²âÊÔ¼¯½øÐлعé ypred = []; for i = 1:size(xdata_test,1) x = xdata_test(i,:); ypred(i,1) = eval(Xstring); end % »æÖÆÔ¤²âͼ figure; plot(ydata_test, 'r-o'); hold on; plot(ypred, 'b-*'); R = corr2(ypred, ydata_test); title(['R = ', num2str(R)]); legend({'����ֵ'; 'Ԥ��ֵ'}); print(gcf, '-dpng', 'fig2.png');