guiyihua.m

mtlab神经网络数据归一化源代码

p=[5.0 100.5 2.9 5 21918 5.0 2.0 3 109.83 1 
   5.0 156.3 3.0 5 72704 5.0 3.0 4 108.75 1 
   5.0 82.7 3.0 4 20067 5.5 3.0 3 104.34 2 
   5.5 99.9 3.0 4 43346 5.7 2.5 5   99.46 1 
   3.0 24.9 3.0 2 10939 8.0 2.0 2.0 99.76 3 
   6.0 37.4 3.4 5 29628 6.5 2.0 4.5 99.76 2 
   6.0 54.9 3.5 5 24750 6.0 3.5 4.5 99.76 1 
   7.0 60.3 3.0 5 9613  4.0 2.0 5.0 108.75 1 
   6.0 21.6 3.0 2 4771  5.0 2.0 4.5 109.83 2 
   4.0 21.5 3.3 5 4413  4.5 4.0 3.3 108.75 2 
   3.0 17.4 3.3 5 4446  4.0 3.0 3.0 104.34 3 
   3.5 10.0 3.0 5 1175  6.0 2.0 3.0 99.46 3 
   5.0 18.5 3.6 5 7967  7.0 2.5 3.0 103.67 2 
   4.0 28.4 3.9 5 5501  6.0 2.0 4.0 108.75 3 
   6.0 48.2 3.5 5 21534 6.0 2.5 4.0 99.46 1 ]';
t=[0.9659 0.09787 0.1604
   0.8805 0.34908 0.5758
   0.9663 0.08387 0.1561
   0.7965 0.19365 0.3373
   0.4501 0.01799 0.0161
   0.6193 0.09678 0.1438
   0.7214 0.08765 0.1391
   0.7577 0.03807 0.0586
   0.6281 0.01898 0.0186
   0.8663 0.01261 0.0240
   0.7184 0.01573 0.0176
   0.6967 0.00769 0.0052
   0.7103 0.02768 0.0388
   0.8286 0.01778 0.0330
   0.6635 0.18960 0.1139]';
for i=1:10;
    P(i,:)=0.1+0.8*(p(i,:)-min(p(i,:)))/(max(p(i,:))-min(p(i,:)))
end
for i=1:3;
    T(i,:)=0.1+0.8*(t(i,:)-min(t(i,:)))/(max(t(i,:))-min(t(i,:)))
end