📄 chengxu.txt
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p=[347.5 0 0 136.49 371.72 10.1 25.98 53.42 10.5 25157 152.59 4 27.29 27.27 27.38 27.51 27.04 76.88 75.99 76.51 76.58 75.76 83 25 20.18
347.4 0 0 136.39 380.68 10.1 25.98 53.42 10.5 25157 135.6 4 27.28 27.26 27.34 27.47 27.04 76.13 75.94 77.09 75.76 75.76 82 25 25.61
346.6 0 0 135.53 381.25 10.1 25.98 53.42 10.5 25157 153.5 4 27.21 27.17 27.25 26.96 26.94 76.37 76.14 76.17 76.77 75.8 82 25 30.47
345.6 0 0 137.24 388.86 10.1 25.98 53.42 10.5 25157 135.3 5 27.51 27.53 27.67 27.18 27.35 78.08 77.65 78.03 77.75 77.35 100 25 20.83
345.3 0 0 135.35 389.46 10.1 25.98 53.42 10.5 25157 134.5 5 27.28 27.26 27.27 26.58 26.96 77.65 77.87 78.05 78.61 77.28 100 25 20.82
344.3 0 0 137.94 385.04 10.1 25.98 53.42 10.5 25157 134.4 5 27.52 27.6 27.8 27.63 27.39 76.83 77.09 78.02 76.76 76.34 100 25 20.8
345.5 0 0 135.11 383.48 10.1 25.98 53.42 10.5 25157 136.1 3 27.08 27.11 27.16 26.97 26.79 76.83 76.79 76.47 76.87 76.52 70 25 20.77
343.1 0 0 135.28 381.91 10.1 25.98 53.42 10.5 25157 136.6 3 27.13 27.08 27.13 27.13 26.81 76.45 76.6 76.98 76.18 75.7 68 25 20.74
346.8 0 0 134.89 379.32 10.1 25.98 53.42 10.5 25157 136.7 3 27.11 27.1 27.09 26.81 26.78 76.48 73.43 76.76 76.2 76.45 70 25 20.8
277.5 0 0 109.36 319.46 10.1 25.98 53.42 10.5 25157 130.6 4 27.35 27.32 27.45 27.24 0 76.35 76.28 77.15 76.96 12.72 59 25 20.57
277.3 0 0 109.36 318.65 10.1 25.98 53.42 10.5 25157 130.3 4 27.35 27.34 27.45 27.22 0 75.88 76.5 77.18 76.7 12.39 60 25 20.56
276.1 0 0 109.3 318.73 10.1 25.98 53.42 10.5 25157 130.4 4 27.35 27.36 27.39 27.2 0 76.23 76.41 76.89 76.68 12.52 60 25 20.58
275.8 0 0 109.49 328.44 10.1 25.98 53.42 10.5 25157 136.4 4 0 27.42 27.48 27.39 27.2 23.45 75.77 76.22 77.4 75.6 61 25 20.61
198.7 0 0 82.13 284.95 10.1 25.98 53.42 10.5 25157 137 4.21 0 27.3 27.39 27.44 0 19.37 75.16 75.35 76.84 38.23 39 25 22.47
213 0 0 82.12 276.87 10.1 25.98 53.42 10.5 25157 130.1 4.27 27.24 27.44 27.44 0 0 79.25 75.43 73.2 10.05 38.94 48 25 21.84
255 0 0 101.3 370 10.2 23.74 50.41 15.65 23565 125.5 4.3 25.6 25.2 25.1 25.4 0 70 74.4 74.3 74.3 77 54 25 16
255 0 0 102.3 278.78 10.2 23.74 50.41 15.65 23565 121 4.05 25.5 25.5 25.6 25.7 0 70.4 69.5 69.2 69.68 0 50 25 25.38
280.7 116.83 0 94.43 312.03 10.1 25.98 53.42 10.5 25157 153.1 4 23.72 23.71 23.73 23.27 0 74.82 74.75 75.02 74.4 13.04 60 25 20.52
277.6 117.57 0 95.67 319.81 10.1 25.98 53.42 10.5 25157 158.2 4 0 23.87 23.98 24.02 23.8 22.92 73.8 73.12 75.53 74.44 63 25 20.53
347.7 150 0 114.96 359.69 10.1 25.98 53.42 10.5 25157 164.8 4 22.99 22.96 23.08 23.16 22.77 71.99 71.95 71.97 71.97 71.81 79 25 20
347.37 150 0 115.2 358.69 10.1 25.98 53.42 10.5 25157 164.7 4 23.08 23.01 23.16 23.13 22.82 72.12 71.63 71.12 72.43 71.39 75 25 19.99
347.79 150 0 115.6 360.01 10.1 25.98 53.42 10.5 25157 165.5 4 23.06 23.04 23.16 23.38 22.96 72.92 71.92 72.31 71.57 71.29 75 25 19.98
351.7 251 0 88.11 287.9 17.6 27.05 47.97 7.38 22713 180.5 3.62 22.3 22.6 22.3 22.7 0 71.9 73 70 73 0 25 67 26.3
350 252.6 0 91.46 286.9 17.6 27.05 47.97 7.38 22713 180.8 3.7 22.68 22.95 22.83 23 0 72 73 69.9 72 0 25 25 26.3
320 197.4 0 101.2 294.51 17.6 27.05 47.97 7.38 22713 171.21 3.75 23.4 25.9 25.9 26 0 77.36 72.45 71.78 72.92 0 25 65 26.8
271.67 246.9 0 71.5 265.9 10.1 25.98 53.42 10.5 25157 180 3.7 0 19.6 20.5 15.6 15.8 0 75.8 72 51.1 67 25 38 22
319.3 250.6 0 96.7 285.46 10.1 25.98 53.42 10.5 25157 176.2 3.72 20.7 25.4 25.4 25.2 0 69.11 71.96 71.68 72.71 0 25 65 22
352.3 253.1 0 108.7 351.38 10.1 25.98 53.42 10.5 25157 178.6 3.84 19.6 24.4 24.3 24.4 16 69.13 72.25 71.94 70.97 67.09 25 69 22
352.11 253.3 0 106.07 350.37 10.1 25.98 53.42 10.5 25157 178.63 3.77 23.4 23.6 24.6 24.5 9.97 70.08 71.25 71.75 71.88 65.41 25 88 22
351.44 257.8 0 104.4 350.65 10.1 25.98 53.42 10.5 25157 181.3 4.91 21 21 21 21.1 20.3 72.19 69.24 69.46 70 69.68 25 83 22
349.76 265.3 0 106.5 349.45 10.1 25.98 53.42 10.5 25157 187.6 3.92 21.3 21.4 21.5 21.5 20.8 72.79 68.41 68.66 70.28 69.31 25 62 22
352.53 251.3 0 102.9 348.98 10.1 25.98 53.42 10.5 25157 192.2 3.08 20.6 20.7 20.7 20.8 20.1 72.74 68.72 69.4 69.74 68.38 25 48 22
349.17 250.3 0 109.3 349.98 10.1 25.98 53.42 10.5 25157 186.3 4.04 23.4 23.5 23.6 23.5 15.3 72.94 70.26 69.81 69.74 67.23 25 60 22
352.36 201.4 0 112.8 353.9 10.1 25.98 53.42 10.5 25157 179.5 4.07 24.4 24.1 24.5 24.8 15 74.08 70.09 71.3 71.43 67 25 64 22
350.89 255.2 8.84 96.3 300.74 10.1 25.98 53.42 10.5 25157 176.7 3.9 23.7 24.1 24.4 24.1 0 73.48 72.4 72.49 71.99 10.38 25 74 22
350.09 255.2 8.84 94.1 300.74 10.1 25.98 53.42 10.5 25157 176.7 4 23 23.6 23.8 23.7 0 73.48 72.4 72.49 71.99 10.38 25 74 22
349.3 255.2 8.84 92.4 300.74 10.1 25.98 53.42 10.5 25157 180.7 3.99 22.8 23.1 23.4 23.1 0 73.48 72.4 72.49 71.99 10.38 25 74 22
348.37 255.2 8.84 98.2 300.74 10.1 25.98 53.42 10.5 25157 180.7 3.92 24.2 24.5 24.8 24.7 0 73.48 72.4 72.49 71.99 10.38 25 74 22
352.53 241.3 29.4 76.8 290.98 10.1 25.98 53.42 10.5 25157 179.9 3.94 20.2 20.3 20.6 15.7 0 70.44 70.26 70.5 69.01 10.77 25 73 22
350.2 241.3 29.04 75.3 290.98 10.1 25.98 53.42 10.5 25157 179.9 3.85 19.8 19.9 20.1 15.5 0 70.44 70.26 70.5 69.01 10.77 25 73 22
350.2 241.3 29.36 74.9 290.98 10.1 25.98 53.42 10.5 25157 179.9 3.97 19.7 19.8 20 15.4 0 70.44 70.26 70.5 69.01 10.77 25 73 22
349.88 343.3 18.76 85 296.22 10.1 25.98 53.42 10.5 25157 178.9 4.09 21.5 21.8 22.1 19.6 0 72.52 71.41 70.75 70.35 11.19 25 73 22
350.5 343.3 18.94 79.8 296.22 10.1 25.98 53.42 10.5 25157 180.57 4.02 20.4 20.5 20.7 18.2 0 72.52 71.41 70.75 70.35 11.19 25 25 22
]';
t=[374.8000 434.2000 430.7000 539.5000 537.5000 386.0000 389.0000 393.0000 362.5000...
362.3000 386.5700 307.0000 238.7500 319.5000 318.4000 281.5700 317.5700 448.2000...
442.2000 231.1000 240.0000 273.3000 186.7000 226.3000 269.0000 344.0000 288.4000...
217.0000 328.3000 296.4000 295.8000 296.2000 296.9000 292.3000 292.4000 314.5000...
315.4000 534.0000 359.5000 441.6000 269.8000 320.0000 298.0000;
92.1631 93.1184 92.1118 92.7834 92.8370 93.4054 93.3788 93.3735...
93.3990 93.4102 93.0734 92.9682 93.3407 93.5934 93.9228 92.1344...
91.8471 91.4750 91.4355 90.7653 90.7121 90.3092 89.7924 91.1475...
90.9936 90.2918 90.4319 90.5767 90.8087 91.0411 90.9960 90.7795...
90.8119 90.8468 90.8880 90.8327 90.7727 92.8370 93.4158 91.4806...
90.9621 90.4456 90.8329
];
u=p;
v=t;
for i=1:25
p(i,:)=(p(i,:)-min(u(i,:)))/(max(u(i,:))-min(u(i,:)));
end
for j=1:2
t(j,:)=(t(j,:)-min(v(j,:)))/(max(v(j,:))-min(v(j,:)));
end
p_model=p(:,1:43);
t_model=t(:,1:43);
um=p_model;
vm= v(:,1:43);
pm=p_model';
tm=t_model';
n=35;
net=newff(minmax(p_model),[n,2],{'tansig','logsig'},'trainlm');
net.trainParam.epochs=1000;
net.trainParam.goal=0.000001;
LP.lr=0.1;
net.trainParam.show=20;
net=train(net,p_model,t_model);
result=sim(net,p_model);
a1=max(v(1,:));a2=max(v(2,:));
b1=min(v(1,:));b2=min(v(2,:));
nox=result(1,:)*(a1-b1)+b1 ;
eff=result(1,:)*(a2-b2)+b2;
plot(1:length(v(1,1:43)),v(1,1:43),'r+:',1:length(nox),nox,'bo:')
title('+为真实值，o为预测值')
title('BP网络模型输出预测曲线');
xlabel('输入样本点');
ylabel('NOx排放量');
figure;
plot(1:length(v(2,1:43)),v(2,1:43),'r+:',1:length(eff),eff,'bo:')
title('+为真实值，o为预测值')
title('BP网络模型输出预测曲线');
xlabel('输入样本点');
ylabel('锅炉效率');
nind=10;
maxgen=500;
preci=10;
ggap=1;
nvar=25;
trace=zeros(2,maxgen);
fieldd=[REP((preci),[1,nvar]);[198.7 0 0 71.5 265.9 10.1 23.74 47.97 7.38 22713 121 3 0 19.6 20 0 0 0 68.41 68.66 10.05 0 25 25 16 ;352.53 343.3 29.4 137.94 389.46 17.6 27.05 53.42 15.65 25157 192.2 5 27.52 27.6 27.8 27.63 27.39 79.25 77.87 78.05 78.61 77.35 100 88 30.47]; REP([1;0;1;1],[1,nvar])];
chrom=CRTBP(nind,nvar*preci);
gen=0;
var=BS2RV(chrom,fieldd);
canshu=[var]';
minv=[198.7 0 0 71.5 265.9 10.1 23.74 47.97 7.38 22713 121 3 0 19.6 20 0 0 0 68.41 68.66 10.05 0 25 25 16]';
maxv=[352.53 343.3 29.4 137.94 389.46 17.6 27.05 53.42 15.65 25157 192.2 5 27.52 27.6 27.8 27.63 27.39 79.25 77.87 78.05 78.61 77.35 100 88 30.47]';
objv=sim(net,canshu);
objv=objv';
a=1./objv(:,1);
b=objv(:,2);
fitnv=1./(RANKING(objv(:,1))+10)+RANKING(objv(:,2));
while gen<maxgen
fitnv=RANKING(objv);
selch=SELECT('SUS',chrom,fitnv,ggap);
selch=RECOMBIN('XOVMP',selch,0.7);
selch=MUT(selch);
variable=BS2RV(selch,fieldd)';
K(25,:)=(variable(25,:)-minv(25))/(maxv(25)-minv(25));
new=K;
ob=sim(net,new);
ob=ob';
objvsel=1./(RANKING(objv(:,1))+0.1)+RANKING(objv(:,2));
[chrom objv]=REINS(chrom,selch,1,1,fitnv,objvsel);
gen=gen+1;
[y,i]=max(objv);
hold on;
plot(var(i),y,'ro');
trace(1,gen)=min(objv);
trace(2,gen)=sum(objv)/length(objv);
fitnv=RANKING(objv);
end
var=BS2RV(chrom,fieldd)';
new=[var'];
yuce=sim(net,new');
nox=yuce(1,:)*(a1-b1)+b1 ;
eff=yuce(1,:)*(a2-b2)+b2;
hold on;
figure(2);
plot(trace(1,:)','bo');
hold on;
plot(trace(2,:)','r-');
grid
legend('解的变化','种群群值的变化')
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