%網(wǎng)絡(luò)初始化
[alphabet,targets]=prprob;
[R,Q]=size(alphabet);
[S2,Q]=size(targets);
S1=10;
P=alphabet;
net= newff(minmax(P),[S1 S2],{'logsig' 'logsig'},'traingdx');
net.LW{2,1}=net.LW{2,1}*0.01;
net.b{2}=net.b{2}*0.01;


T=targets;
%網(wǎng)絡(luò)訓(xùn)練參數(shù)設(shè)置
net.performFcn='sse';
net.trainParam.goal=0.1;
net.trainParam.show=20;
net.trainParam.epochs=5000;
net.trainParam.mc=0.95;
%開始對無誤差輸入向量進(jìn)行訓(xùn)練
[net,tr]=train(net,P,T);
%網(wǎng)絡(luò)訓(xùn)練參數(shù)設(shè)置,并對有誤差輸入向量進(jìn)行訓(xùn)練
netn=net;
netn.trainParam.goal=0.6;
netn.trainParam.epochs=300;
T=[targets targets targets targets];
pause
for pass=1:10
P=[alphabet,alphabet,...
(alphabet+randn(R,Q)*0.1),...
(alphabet+randn(R,Q)*0.2)];
[netn,tr]=train(netn,P,T);
pause
end
%網(wǎng)絡(luò)再次對無誤差輸入向量進(jìn)行訓(xùn)練
P=alphabet;
T=targets;
net.performFcn='sse';
net.trainParam.goal=0.1;
net.trainParam.show=20;
net.trainParam.epochs=5000;
net.trainParam.mc=0.95;
[net,tr]=train(net,P,T);
pause
%測試網(wǎng)絡(luò)的容錯性
noise_range=0:0.05:0.5;
max_test=100;
T=targets;
for i=1:11
  noiselevel(i)=noise_range(i);
  errors1(i)=0;
  errors2(i)=0;
  for j=1:max_test
    P=alphabet+randn(35,26)*noiselevel(i);
    % 測試未經(jīng)誤差訓(xùn)練的網(wǎng)絡(luò) 
    A=sim(net,P);


    AA=compet(A);
    errors1(i)=errors1(i)+sum(sum(abs(AA-T)))/2;
    % 測試經(jīng)過誤差訓(xùn)練的網(wǎng)絡(luò)
    An=sim(netn,P);
    AAn=compet(An);
    errors2(i)=errors2(i)+sum(sum(abs(AAn-T)))/2;
   end
end
pause
figure
plot(noise_range,errors1*100,'--',noise_range,errors2*100);
title('識別錯誤率');
xlabel('噪聲指標(biāo)');
ylabel('未經(jīng)誤差訓(xùn)練的網(wǎng)絡(luò) - -   經(jīng)過誤差訓(xùn)練的網(wǎng)絡(luò)---');
%對實(shí)際含噪聲的字母進(jìn)行識別
for index=1:5:26
noisyJ=alphabet(:,index)+randn(35,1)*0.2;
figure;
plotchar(noisyJ);
A2=sim(net,noisyJ);
A2=compet(A2);
answer=find(compet(A2)==1);
figure;
plotchar(alphabet(:,answer));
end