p50.m

竞争网络的matlab程序

% p50.m
% 赋值、初始化
P=-1:0.1: 1;  P2=-1:.025:1;
T=[-0.96 -.577 -.0729 .377 .641 .66 .461 .1336...
    -.201 -.434 -.5 -.393 -.1647 .0988 .3072...
    .396 .3449 .1816 -.0312 -.2183 -.3201];
[R,Q]=size(P), S1=5;
[S2,Q]=size(T),
%[S2,Q]=size(T2),
%画误差曲面
[W1,B1]=rands(S1,R);[W2,B2]=rands(S2,S1);
A1=tansig(W1*P,B1);A2=purelin(W2*A1,B2);
A22=purelin(W2*tansig(W1*P2,B1),B2);
h0=figure;
plot(P,T,'r+',P2,A22);axis([-1,1,-1,1]),title('初始网络');
E=T-A2;
SSE=sumsqr(E)
%训练权值
disp_freq=1000;
max_epoch=4000;
err_goal=0.02;      % 期望误差
lr=0.01
for epoch=1:max_epoch
    if SSE<err_goal
        epoch=epoch-1;break, end
   D2=deltalin(A2,E);
   D1=deltatan(A1,D2,W2);
   [dW1,dB1]=learnbp(P,D1,lr);
   [dW2,dB2]=learnbp(A1,D2,lr);
   dW1=W1+dW1;B1=B1+dB1;
   dW2=W2+dW2;B2=B2+dB2;
   A1=tansig(W1*P,B1);A2=purelin(W2*A1,B2);
   E=T-A2;
   SSE=sumsqr(E);
   % SSE=sumsqr(T-purelin(W2*tansig(W1*P2,B1),B2);
  % if rem(epoch,1000)==0
   %    n=epoch/1000
    %   A22=purelin(W2*tansig(W1*P2,B1),B2);
     %  h(n)=figure
      %plot(P,T,'c+',P2,A22);axis(-1,1,-1,1),
      %title('epoch=epoch');
      %end
       if epoch==1000
            epoch
          A22=purelin(W2*tansig(W1*P2,B1),B2);
          h1=figure
          plot(P,T,'g+',P2,A22);axis([-1,1,-1,1]),
          title('epoch=1000');
          SSE
          pause
      end
        if epoch==6000
          epoch
            A22=purelin(W2*tansig(W1*P2,B1),B2);
          h6=figure
          plot(P,T,'b+',P2,A22);axis([-1,1,-1,1]),
          title('epoch=6000');
          SSE
      end
        if epoch==2000
          A22=purelin(W2*tansig(W1*P2,B1),B2);
          h2=figure, epoch
          plot(P,T,'y+',P2,A22);axis([-1,1,-1,1]),
          title('epoch=2000');SSE
          pause
      end
        if epoch==3000
          A22=purelin(W2*tansig(W1*P2,B1),B2);
          h3=figure
          epoch
          plot(P,T,'c+',P2,A22);axis([-1,1,-1,1]),
          title('epoch=3000');
          SSE3=SSE
          pause
      end
        if epoch==4000
          A22=purelin(W2*tansig(W1*P2,B1),B2);
          h4=figure, epoch
          plot(P,T,'c*',P2,A22);axis([-1,1,-1,1]),
          title('epoch=epoch');
          SSE4=SSE
      end
        if epoch==5000
          A22=purelin(W2*tansig(W1*P2,B1),B2);
          h5=figure, epoch
          plot(P,T,'c+',P2,A22);axis([-1,1,-1,1]),
          title('epoch=5000');
          SSE5=SSE
      end
end
A22=purelin(W2*tansig(W1*P2,B1),B2);
       h7=figure
      plot(P,T,'c+',P2,A22);axis([-1,1,-1,1]),
      title('epoch=epoch  终止网络');
      SSEZ=SSE
      epoch
  end
 % 最佳学习速率
 
  % 最佳学习速率
  %设置暂停方式为秒
%A=purelin(W*P,B)
%E=T-A2;
%SSE=sumsqr(E);  %误差记录
%e%rrors=[SSE];
%for epoch=1:max_epoch
 %      if SSE<err_goal,epch=epoch-1;break,R3=R,
 %end
 %   LW=W;LB=B;
  %  [DW,DB]=learnwh(P,E,lr);
   % W=W+DW;B=B+DB;
    %A=purelin(W*P,B); E=T-A;  SSE=sumsqr(E);  %学习后的再次表达式
    %errors=[errors,SSE];
%训练过程中的显示
   
%TP=[disp_freq max_epoch err_goal lr];
%[W,B,epochs,error]=trainwh[W,B,P,T,TP]  % 进行线形网络权值训练
%V=[-2 2 -2 2];
%plotpv(P,T,V);
%hold on
%plotpc(W,B)
%hold off
%end