rbf_yuchuli_xunlian.m

采用神经网络实现分类器的训练

%径向基网络（预处理,边聚类，边训练）
clear 
c=load('ccc.txt');
q1=3;%q1为常向量，表示神经元的个数
Ir=0.1521; max_epoch=1500;
EE=zeros(length(q1),max_epoch);
res=zeros(size(q1));%res将要存储误差向量，这里先置零 
 aa=1;
clear oi ok wwji wwkj b1 b2 deltak deltao yy result cout1 count2 E
 tic 
ceshiyangben=c;err_goal=0.05;
  for i=1:59
  target(i,:)=[0 0 1];
  end
  for i=60:131
  target(i,:)=[0 1 0];
  end
  for i=132:178
  target(i,:)=[1 0 0];
  end
  E=[];T=target';%mubiaoxiangliang
  q=q1(aa);
  [m,n]=size(ceshiyangben);[s2,s1]=size(T); 
 for i=2:n;
   for j=1:m
   ceshiyangben(j,i)=ceshiyangben(j,i)/max(ceshiyangben(:,i));
   end
  end %归一化1
  %ceshiyangben=exp(2*ceshiyangben);
  ceshiyangben=log10(10*ceshiyangben+1);
  for i=2:n;
   for j=1:m
   ceshiyangben(j,i)=ceshiyangben(j,i)/max(ceshiyangben(:,i));
   end
  end %归一化2
   Wki=0.2*rands(3,q); %
   b2=0.1*rands(3,1);% 输出层偏值 
   wwkj=[b2 Wki];
   P=ceshiyangben';
   count1=zeros(1,3);
   count2=zeros(1,3);
   w=P;
  %w=w(:,randperm(178));         %随机改变样本顺序
  
   z1=w(:,1);z2=w(:,2);z3=w(:,3); I=1;   %1
for kk=1:max_epoch 
     w1=[];w2=[];w3=[];
        for i=1:178
           if (sqrt(sum(w(:,i)-z1)^2)<sqrt(sum(w(:,i)-z2)^2))&(sqrt(sum(w(:,i)-z1)^2)<sqrt(sum(w(:,i)-z3)^2))
                   w1=[w1,w(:,i)];[h,n1]=size(w1);
              else  if (sqrt(sum(w(:,i)-z2)^2)<sqrt(sum(w(:,i)-z1)^2))&(sqrt(sum(w(:,i)-z2)^2)<sqrt(sum(w(:,i)-z3)^2))
                    w2=[w2,w(:,i)];[h,n2]=size(w2);
                   else w3=[w3,w(:,i)];[h,n3]=size(w3);
                   end
          end    
      end
  % [n1 n2 n3]
     r1=sqrt(sum((P-z1*ones(1,178)).^2));r2=sqrt(sum((P-z2*ones(1,178)).^2));r3=sqrt(sum((P-z3*ones(1,178)).^2));  % ||X-W||
     sigma=1*max([sum((z1-z2).^2),sum((z1-z3).^2),sum((z3-z2).^2)])/sqrt(2*3);
     ojj1=exp(-r1.^2/(2*sigma^2));               %高斯函数
     ojj2=exp(-r2.^2/(2*sigma^2));
     ojj3=exp(-r3.^2/(2*sigma^2));
     ojj=[ojj1;ojj2;ojj3];
     oj1=[-ones(178,1) ojj'];
     netk=wwkj*oj1';
     oo=logsig(netk);%3-178
     errr=T-oo;   
     sse=mse(errr);
     deltao=errr.*oo.*(ones(3,178)-oo);%3-178
     if(sse<err_goal)
         break;
     end
     wwkj=wwkj+Ir*(deltao*oj1);
     E=[E,sse];
     z1=mean(w1')';z2=mean(w2')';z3=mean(w3')';
     w11=w1;w22=w2;w33=w3;
     %[a,i]=size(z1); [a,k]=size(z2); [a,m]=size(z3);
     %if (z1(:,i)==z1(:,i-1))&(z2(:,k)==z2(:,k-1))&(z3(:,m)==z3(:,m-1))
        %break
        %end  
 end
  res(aa)=norm(errr);
  EE(aa,:)=E;
  toc
   %yangbenceshi 
    r1=sqrt(sum((P-z1*ones(1,178)).^2));r2=sqrt(sum((P-z2*ones(1,178)).^2));r3=sqrt(sum((P-z3*ones(1,178)).^2)); 
     ojjj1=exp(-r1.^2/(2*sigma^2));  %11 178
     ojjj2=exp(-r2.^2/(2*sigma^2));
     ojjj3=exp(-r3.^2/(2*sigma^2));
     ojjj=[ojj1;ojj2;ojj3];
     ojj1=[-ones(178,1) ojj'];
     netkk=wwkj*oj1';
     ooo=logsig(netk);%3-178
     for j=1:s2
        for i=1:178
       yy(j,i)=round(oo(j,i));
        end
     end
  for h=1:178
   if yy(:,h)==T(:,h)
      if h<=59
      result(1,h)=1;count1(1,1)=count1(1,1)+1;
      elseif h<=131&h>59
      result(1,h)=2;count1(1,2)=count1(1,2)+1;
      elseif h<=178&h>131
      result(1,h)=3;count1(1,3)=count1(1,3)+1;
      end
    else
    result(1,h)=0;   
   end
  end
    Rresult(aa,:)=result;

  for hh=1:178
    if yy(:,hh)==[0 0 1]'
        count2(1,1)=count2(1,1)+1;
    elseif  yy(:,hh)==[0 1 0]' 
        count2(1,2)=count2(1,2)+1;
    elseif  yy(:,hh)==[1 0 0]' 
       count2(1,3)=count2(1,3)+1;  
    end
  end
  Re(aa,:)=count1./[59 72 47]
  Pr(aa,:)=count1./count2
  for dd=1:length(q1)
    plot(EE(dd,:))
    hold on
  end
 number=find(res==min(res));
w11;
w22;
w33;
Re4=Re;
Pr4=Pr;
figure
plot(Rresult(1,:)')