bp1.m

自己编写的小波神经网络的代码。已经得到成功的应用

a=[24.88 24.75 25.95 26.63 25.10 25.20 26.25 27.13 26.63 27.25 27.88 28.23 28.00 27.75 28.65 28.80 26.80 27.00 27.00 26.00 27.30 28.00 26.75 25.90 25.40 25.50];%
p1=a(1:5);t1=a(6);
p2=a(2:6);t2=a(7);
p3=a(3:7);t3=a(8);
p4=a(4:8);t4=a(9);
p5=a(5:9);t5=a(10);
p6=a(6:10);t6=a(11);
p7=a(7:11);t7=a(12);
p8=a(8:12);t8=a(13);
p9=a(9:13);t9=a(14);
p10=a(10:14);t10=a(15);
p11=a(11:15);t11=a(16);
p12=a(12:16);t12=a(17);
p13=a(13:17);t13=a(18);
p14=a(14:18);t14=a(19);
p15=a(15:19);t15=a(20);
p16=a(16:20);p17=a(17:21);p18=a(18:22);p19=a(19:23);p20=a(20:24);p21=a(21:25);
p=[p1' p2' p3' p4' p5' p6' p7' p8' p9' p10' p11' p12' p13' p14' p15' p16' p17' p18' p19' p20' p21'];
[pn,minp,maxp]=premnmx(p);
%A=[pn(1:5);pn(6:10);pn(11:15);pn(16:20);pn(21:25);pn(26:30);pn(31:35);pn(36:40);pn(41:45);pn(46:50);pn(51:55);pn(56:60);pn(61:65);pn(66:70);pn(71:75)];
A=pn(:,1:15);
t=[t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12 t13 t14 t15];
net=newff(minmax(pn),[9 1],{'logsig' 'purelin'},'trainlm','learngdm','sse');
net.trainParam.show=50;
net.trainParam.lr=0.3;
net.trainParam.epochs=2000;
net.trainParam.goal=1e-18;
[net,tr,y,error]=train(net,A,t);%tr训练步数，网络误差
b=sim(net,pn);%对pn 进行仿真 ，结果存在 b 中