letter_1.zip_letters neural_噪声 识别_噪声识别_字母识别_网络识别字母

%初始化 [alphabet,targets]=prprob; [R,Q]=size(alphabet); [S2,Q]=size(targets); S1=10; [R,Q]=size(alphabet); [S2,Q]=size(targets); 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; %无噪声训练 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); %有噪声训练 netn=net; net.trainParam.goal=0.6; net.trainParam.epochs=300; T=[targets targets targets targets]; for pass=1:10 P=[alphabet,alphabet,... (alphabet+randn(R,Q)*0.1),(alphabet+randn(R,Q)*0.2)]; [netn,tr]=train(net,P,T); end %再次有噪声训练 net.trainParam.goal=0.1; net.trainParam.epochs=500; net.trainParam.show=5; P=alphabet; T=targets; [netn,tr]=train(netn,P,T); %系统性能 noise_range=0:.05:.5; max_test=100; network1=[]; network2=[]; T=targets; for noiselevel=noise_range errors1=0; errors2=0; for i=1:max_test P=alphabet+randn(35,26)*noiselevel; A=sim(netn,P); AA=compet(A); errors1=errors1+sum(sum(abs(AA-T)))/2; An=sim(netn,P); AAn=compet(An); errors2=errors2+ sum(sum(abs(AAn-T)))/2; end network1=[network1 errors1/26/100]; network2=[network2 errors1/26/100]; end plot(noise_range,network1*100,'--',noise_range,network2*100); title('识别误差'); xlabel('噪声指标'); ylabel('无噪声训练网络--有噪声训练网络---'); noisyJ=alphabet(:,1)+randn(35,1)*0.2; plotchar(noisyJ); A2=sim(net,noisyJ); A2=compet(A2); answer=find(compet(A2)==1); plotchar(alphabet(:,answer)); noisyJ=alphabet(:,10)+randn(35,1)*0.2; plotchar(noisyJ); A2=sim(net,noisyJ); A2=compet(A2); answer=find(compet(A2)==1); plotchar(alphabet(:,answer)); noisyJ=alphabet(:,23)+randn(35,1)*0.2; plotchar(noisyJ); A2=sim(net,noisyJ); A2=compet(A2); answer=find(compet(A2)==1); plotchar(alphabet(:,answer));