rbfprediction1.m

RBF径向基神经网络

 % 河段浅滩演变RBF预测
% 10个样本输入向量p:4*10,输出向量t:2*10
p=[1520  510 5.155 33.88;
   1468  521 5.321 35.79;
   2412 1140 5.320 25.89;
   1750  129 4.700 23.80;
   1688  361 4.865 27.08;
   1607  489 5.100 28.9;
   1200  127 4.56 19.84;
   1990  148 4.89 29.373;
   1509  511 5.12 34.3;
   1730  133 4.46 23.6]';
   
t=[0.7 1.9;
   0.6 1.798; 
   0.8 1.289; 
   1.0 1.68;
   0.8 1.149;
   1.03 1.72;
   1.8 1.095;
   0.9 1.230;
   0.8 1.35;
   1.4 1.201]';
%归依化
for i=1:4
       P(i,:)=(p(i,:)-min(p(i,:)))/( max(p(i,:))-min(p(i,:)) ); 
end   
for i=1:2
       T(i,:)=( t(i,:)-min(t(i,:)))/( max(t(i,:))-min(t(i,:)) ); 
end   

P_test=[P(:,1) P(:,3) P(:,5) P(:,7) P(:,8) P(:,10) ];
T_test=[T(:,1) T(:,3) T(:,5) T(:,7) T(:,8) T(:,10) ];

for spread=1:5

    net=newrbe(P,T,spread);
    temp=sim(net,P); 
    y(2*spread-1,:)=temp(1,:);
    y(2*spread,:)=temp(2,:);
end