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%ÊäÈëÏòÁ¿ p=[97 28 12 32 18 3 0 3 10 3 6 14 8 20 2 11 24 0 9 6 3 5 3 1 3 14 2 10 2 173 17 5 1 61 7; 66 29 11 34 13 3 0 1 14 4 12 11 8 12 2 13 20 0 4 3 4 5 4 1 0 12 0 9 3 250 4 3 0 58 3; 61 30 12 35 6 2 0 1 11 7 5 9 11 17 2 3 22 2 4 3 5 5 4 2 0 16 5 12 4 237 11 4 1 59 2; 79 28 18 43 7 6 1 5 9 6 6 20 17 30 1 12 20 0 9 5 2 5 5 3 2 16 5 14 3 249 20 3 0 56 3; 94 38 15 39 9 4 1 3 13 2 12 15 10 27 2 17 16 2 4 3 4 9 4 2 0 20 7 10 0 230 37 2 0 68 6; 106 53 16 44 17 3 0 2 15 3 18 12 7 19 11 0 32 0 4 9 2 6 6 3 2 8 0 10 4 331 25 1 1 104 5; 106 55 18 21 17 3 1 2 14 3 11 16 11 29 1 13 12 0 3 8 3 10 7 3 5 5 7 7 1 529 50 7 1 141 0; 113 75 19 29 42 9 2 3 26 0 13 21 5 26 2 5 27 1 5 20 2 5 18 4 4 1 3 17 5 643 30 2 7 120 4; 110 62 18 30 18 6 0 5 17 1 18 10 14 24 1 6 18 0 3 14 6 6 24 5 5 11 6 18 4 573 42 2 4 122 4; 114 93 23 28 29 6 0 9 23 4 8 25 15 36 0 4 28 3 5 16 4 2 36 3 2 7 9 12 7 699 54 8 8 139 8; 152 107 23 48 50 15 2 4 29 4 20 29 13 40 1 7 42 3 7 28 4 18 50 6 3 13 3 23 3 785 30 7 5 207 10]'; %Ä¿±êÏòÁ¿ t=[79 28 18 43 7 6 1 5 9 6 6 20 17 30 1 12 20 0 9 5 2 5 5 3 2 16 5 14 3 249 20 3 0 56 3; 94 38 15 39 9 4 1 3 13 2 12 15 10 27 2 17 16 2 4 3 4 9 4 2 0 20 7 10 0 230 37 2 0 68 6; 106 53 16 44 17 3 0 2 15 3 18 12 7 19 11 0 32 0 4 9 2 6 6 3 2 8 0 10 4 331 25 1 1 104 5; 106 55 18 21 17 3 1 2 14 3 11 16 11 29 1 13 12 0 3 8 3 10 7 3 5 5 7 7 1 529 50 7 1 141 0; 113 75 19 29 42 9 2 3 26 0 13 21 5 26 2 5 27 1 5 20 2 5 18 4 4 1 3 17 5 643 30 2 7 120 4; 110 62 18 30 18 6 0 5 17 1 18 10 14 24 1 6 18 0 3 14 6 6 24 5 5 11 6 18 4 573 42 2 4 122 4; 114 93 23 28 29 6 0 9 23 4 8 25 15 36 0 4 28 3 5 16 4 2 36 3 2 7 9 12 7 699 54 8 8 139 8; 152 107 23 48 50 15 2 4 29 4 20 29 13 40 1 7 42 3 7 28 4 18 50 6 3 13 3 23 3 785 30 7 5 207 10; 111 124 15 31 72 14 0 2 27 2 12 20 7 29 0 42 17 2 7 11 2 8 12 5 6 4 3 23 1 703 25 4 5 170 4; 144 116 24 52 52 12 1 4 12 3 11 28 14 45 2 19 18 0 5 17 6 11 14 4 13 15 5 31 3 674 24 6 9 166 7]' ; %¹éÒ»»¯ u=t; %%%%%%¹éÒ»µ½ 0 1 Ö®¼ä for i=1:35   p(i,=(p(i,-min(p(i,))/(max(p(i,)-min(p(i,)); end for i=1:35   t(i,=(t(i,-min(t(i,))/(max(t(i,)-min(t(i,)); end %ѵÁ· %%%%%%%%%%thresholdµÄ×÷ÓÃÊÇÏÞ¶¨ÊäÈëµÄ·¶Î§£¬ÒòΪÒѾ­×ö¹ý¹éÒ»»¯£¬ËùÒÔÊäÈëµÄ·¶Î§ÔÚ0 1Ö®¼ä %%%%%%%%%%Õâ¸ö·¶Î§±ÈÊäÈëµÄ·¶Î§´óÊÇ¿ÉÒԵģ¬¾Ù¸öÀý×Ó£¬±È·½Ëµ¹éÒ»»¯µÄÊý¾ÝÊÇÔÚ[0.1 0.9]Ö®¼ä %%%%%%%%%%·¶Î§ÓÃ[0 1]ÊÇ¿ÉÒԵģ¬±È¹éÒ»»¯ºóµÄ·¶Î§Èç¹ûСµÄ»°£¬¾Í»á³öÎÊÌâ threshold=[0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1; 0 1]; net=newff(threshold,[19,35],{'tansig','logsig'},'trainlm'); %%%%%%%%%%19Òþ²ãÊý Òþ²ãÊýµÄÈ·¶¨Óм¸¸ö¾­Ñ鹫ʽ£¬Ò²¿ÉÒÔÓÃÊÔËã·¨ net.trainParam.epochs=1000; %ѵÁ·´ÎÊý net.trainParam.goal=0.001; %ѵÁ·½áÊøµÄÄ¿±ê LP.lr=0.1; %Õâ¸öÓ¦¸ÃÊÇѧϰÂÊ net.trainParam.show=20; P=[p(:,1)+p(:,2)+p(:,3) p(:,2)+p(:,3)+p(:,4) p(:,3)+p(:,4)+p(:,5) p(:,4)+p(:,5)+p(:,6) p(:,5)+p(:,6)+p(:,7) p(:,6)+p(:,7)+p(:,8) p(:,7)+p(:,8)+p(:,9) p(:,8)+p(:,9)+p(:,10) p(:,9)+p(:,10)+p(:,11)]; T=[t(:,1) t(:,2) t(:,3) t(:,4) t(:,5) t(:,6) t(:,7) t(:,8) t(:,9)]; %ÊäÈëÑù±¾µÄÊäÈëºÍÄ¿±ê net=train(net,P,T); %ѵÁ·ÍøÂç test=[ t(:,7)+t(:,8)+t(:,9)]; out=sim(net,test); %·ÂÕæÔ¤²â %·´¹éÒ»»¯ for i=1:35   predict(i)=out(i)* (max(u(i,)-min(u(i,))+ min(u(i,); end predict %½á¹ûÕûÀí predictsum=predict(1); sum=u(1,10); for i=1:34   predictsum= predictsum +predict(i+1);   sum=sum+u(i+1,10); end   sum predictsum   sumerror= predictsum-sum %»æÖÆÔ¤±¨ÇúÏß x=[t(:,10)]; a=x-out; for i=1:35   error=a* (max(u(i,)-min(u(i,))+ min(u(i,); end X=1:35; figure(1); plot(X,error) xlabel('µØÉϹÜÍø¼±ÐÞ×÷ÒµÀàÐÍ£¨X£©'); ylabel('×÷ÒµÁ¿Ô¤²âÎó²î£¨Y£©'); figure(2); plot(X, predict,'r*',X,u(:,10),'bo') xlabel('µØÉϹÜÍø¼±ÐÞ×÷ÒµÀàÐÍ£¨X£©'); ylabel('×÷ÒµÁ¿£¨Y£©'); text (23,predict(30), 'predicted data'); text(25.5,u(30,10), 'real data');

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