大家帮忙看看，不懂为什么这么编？

QQ1641085143

data=[-0.22914-0.059577-0.88853-0.25353-0.284220.096958;
-0.482640.636450.025257-0.363270.47082-0.087841;
-0.42280.285860.341070.048845-0.766110.18407;
-0.38717-0.467720.14487-0.23132-0.089108-0.74088;
-0.40248-0.53820.19561-0.229170.245250.63041;
-0.47317-0.035252-0.185180.833870.20625-0.051838];
x=data(:,1:5);t=data(:,6);
c1=x(1:3,:);  %初值
%第一次分类
for i=1:1:6
     for j=1:1:3
    d1(i,j)=(x(i,:)-c1(j,:))*((x(i,:)-c1(j,:))');
end
end
% x1   d1=[0   1.9658 1.9924;     
% x2    1.96580     1.9261;
% x3    1.99241.9261  0
% x4    1.2981.5735 1.1445
% x5    1.71541.4841 1.8008
% x6    1.97791.9987 1.9443]     
%类1（x1)
%类2（x2,x5)
%类3（x3,x4,x6)
%中心c1=[-0.22914 -0.059577 -0.88853 -0.25353 -0.28422
%       -0.482640.63645   0.025257   -0.36327   0.47082
%       -0.4228    0.28586   0.34107   0.048845   -0.76611]


%第二次聚类
%新的中心c
c2=[x(1,:);0.5*(x(2,:)+x(5,:));(x(3,:)+x(4,:)+x(6,:))./3];
%c2=[ -0.22914  -0.059577 -0.88853 -0.25353 -0.28422
%     -0.44256  0.049124 0.11043    -0.29622  0.35803
%    -0.42772  -0.072373  0.10025  0.21713-0.21632]
%求d2
for i=1:1:6
     for j=1:1:3
    d2(i,j)=(x(i,:)-c2(j,:))*((x(i,:)-c2(j,:))');
end
end
%x1      d2=[0    1.4696  1.2434
%x2          1.96580.37103  1.3201
%x3          1.99241.4924  0.51694
%x4          1.2980.47553  0.37723
%x5          1.71540.37103  0.63896
%x6          1.97791.3956  0.64385]
%聚类结果
%类1(x1)
%类2(x2,x5）
%类3(x3,x4,x6)
%
%可知，c不再变化，故，分类结束
%
%最后的结果：c＝[ -0.22914  -0.059577 -0.88853 -0.25353 -0.28422
%                -0.44256  0.049124 0.11043    -0.29622  0.35803
%                -0.42772  -0.072373  0.10025  0.21713-0.21632]
%  求RBF基函数的宽度delta
dd2=d2';
dsum=sum(dd2);
delta=[dsum(1) 0.5*(dsum(2)+dsum(5)) (dsum(2)+dsum(4)+dsum(6))./3];
%delta=[2.7133.19123.275]
%据上述可知，隐含层数（采用高斯核函数）为3，输出层为线性输出
for j=1:1:3
for i=1:1:6
a(j,i)=((x(i,:)-c2(j,:)))*((x(i,:)-c2(j,:))');
    a(j,i)=(-a(j,i)./delta(j));
end
end
P=1:1:6;
T=t;
%figure;subplot(2,2,1);plot(P,t);title('待逼近的函数样本点');
%axis([1,6,-1,1])
  p=a;
r=radbas(p);
err_goal=0.99;
sc=1;
net=newrb(p,T',err_goal,sc);
%NEWRB, neurons = 0, SSE = 0.999213
Y=sim(net,p);
%axis([1,6,-0.4,0.4]);
figure;
plot(P,T,'r');
hold on;
plot(P,Y,':*');
title('RBF网络拟合曲线图11');
legend('化验值','估计值');
ylabel('淀粉利用率（％）');
xlabel('样本个数');
axis([1,6,-1,1]);