建模.asv

RBF实现神经网络预测程序

%主元分析后的数据集124*6——6×6
%建模数据
m_data=[0.17291	0.61562	-0.23182	0.72739	-0.085257	-0.031892;
-0.51861	-0.16758	-0.19343	0.11447	-0.80028	0.10954;
-0.21276	-0.65128	-0.070456	0.61803	0.36643	-0.096825;
0.5657	-0.29331	-0.19992	0.06454	-0.14817	0.72654;
0.56077	-0.28381	-0.17084	-0.01873	-0.35697	-0.66935;
0.14631	-0.046753	0.91363	0.26708	-0.26198	0.041455];
%RBF建模
% K-means法确定RBF函数的中心、宽度和输出权值
x=m_data(:,1:5);y=m_data(:,6);

c=x;
for i=1:1:6
   for j=1:1:6
    h(i,j)=(x(i,:)-c(j,:))*((x(i,:)-c(j,:))');
    end
end
for j=1:1:6
       cc(j)=min(h(j));
   end
fai=exp(-(x-c)*((x-c)')/(2*delta));??
(??for i=1:1:6
   for j=1:1:6
fai=exp(-(x(i,:)-c(j,:))*((x(i,:)-c(j,:))')/(2*delta)); [fai]=1*1??
end
end??
 [ww,bint,err,errint,states]=regress(y,fai);
yy=purelin(fai*ww);

x=m_data(:,1:5);y=m_data(:,6);
 p=x';
t=y';net=newrbe(p,t);
yy=sim(net,p);figure;plot(t,'r');hold on;plot(yy,'*');