train_zaixian_2.m

基于matlab的神经网络训练方法

%加入动量项-在线处理-2个隐含层
clear oj ok of oo  wji wkj wfk wof b1 b2 b3 b4 deltak deltai yy result epoch
ceshiyangben=ccc;
P=ceshiyangben';
Ir=0.0121;%学习速率
err_goal=0.05;arfa=0.07;
 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
T=target';%目标向量生成
[m,n]=size(ceshiyangben);[s2,s1]=size(T);
for i=1:n
    A=mean2(ceshiyangben(:,i));
    B=std(ceshiyangben(:,i));
  ceshiyangben(:,i)=(ceshiyangben(:,i)-A)./B;
end%对样本进行归一化
q1=11;q2=5;
wji=0.1*rands(q1,13); wkj=0.1*rands(q2,q1); wok=0.1*rands(3,q2);
b1=0.1*rands(q1,1);b2=0.1*rands(q2,1);b3=0.1*rands(3,1);;%—随机给定隐含层、输出层偏值 
E=[ ];%记录每次训练的误差初始化
max_epoch=178;CCji=zeros(q1,13);CCkj=zeros(q2,q1);CCok=zeros(3,q2);
for I=1:1000
    for i=1:m
    netj=wji*P(:,i)-b1;
    oj=logsig(netj);
     netk=wkj*oj-b2;
    ok=logsig(netk);
     neto=wok*ok-b3;
    oo=logsig(neto); 
    errr=T(:,i)-oo;   
    sse=mse(errr);
   deltao=oo.*(ones(3,1)-oo).*errr;
   deltak=ok.*(ones(q2,1)-ok).*(wok'*deltao);
   deltaj=oj.*(ones(q1,1)-oj).*(wkj'*deltak); 
   deltawji=(deltaj*P(:,i)');
   wji=wji+Ir*deltawji+arfa*CCji
   b1=b1+Ir*deltaj;CCji=deltawji;
   deltawkj=(deltak*oj');wkj=wkj+Ir*deltawkj+arfa*CCkj; b2=b2+Ir*deltak;CCkj=deltawkj;
   deltawok=(deltao*ok');wok=wok+Ir*deltawok+arfa*CCok; b3=b3+Ir*deltao; CCok=deltawok;
    end
end    

for i=1:m
 netjj=wji*P(i,:)';
    ojj=logsig(netjj-b1);
     netkk=wkj*oj;
    okk=logsig(netkk-b2);
     netff=wfk*ok;
    off=logsig(netff-b3);
     netoo=wof*off;
    ooo(:,i)=logsig(netoo-b4);
end
  for j=1:s2
        for i=1:178
    yy(j,i)=round(ooo(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
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=count1./[59 72 47]
Pr=count1./count2
plot(E,'r-')
figure 
plot(result)