matlab数字识别

% 数字识别 number0=[0 1 1 1 0.... 1 0 0 0 1.... 1 0 0 0 1.... 1 0 0 0 1.... 1 0 0 0 1.... 1 0 0 0 1.... 0 1 1 1 0]'; number1=[0 0 1 0 0.... 0 0 1 0 0.... 0 0 1 0 0.... 0 0 1 0 0.... 0 0 1 0 0.... 0 0 1 0 0.... 0 0 1 0 0]'; number2=[1 1 1 1 1.... 0 0 0 0 1.... 0 0 0 1 0.... 0 0 1 0 0.... 0 1 0 0 0.... 1 0 0 0 0.... 1 1 1 1 1]'; number3=[1 1 1 1 1.... 0 0 0 0 1.... 0 0 0 0 1.... 1 1 1 1 1.... 0 0 0 0 1.... 0 0 0 0 1.... 1 1 1 1 1]'; number4=[1 0 0 0 1.... 1 0 0 0 1.... 1 0 0 0 1.... 1 1 1 1 1.... 0 0 0 0 1.... 0 0 0 0 1.... 0 0 0 0 1]'; number5=[1 1 1 1 1.... 1 0 0 0 0.... 1 0 0 0 0.... 1 1 1 1 1.... 0 0 0 0 1.... 0 0 0 0 1.... 1 1 1 1 1]'; number6=[1 1 1 1 1.... 1 0 0 0 0.... 1 0 0 0 0.... 1 1 1 1 1.... 1 0 0 0 1.... 1 0 0 0 1.... 1 1 1 1 1]'; number7=[1 1 1 1 1.... 0 0 0 0 1.... 0 0 0 0 1.... 0 0 0 0 1.... 0 0 0 0 1.... 0 0 0 0 1.... 0 0 0 0 1]'; number8=[1 1 1 1 1.... 1 0 0 0 1.... 1 0 0 0 1.... 1 1 1 1 1.... 1 0 0 0 1.... 1 0 0 0 1.... 1 1 1 1 1]'; number9=[1 1 1 1 1.... 1 0 0 0 1.... 1 0 0 0 1.... 1 1 1 1 1.... 0 0 0 0 1.... 0 0 0 0 1.... 1 1 1 1 1]'; alphabet=[number0,number1,number2,number3,number4,number5,number6,number7,number8,number9]; targets = eye(10); %[alphabet,targets] = date; P =alphabet; %alphabet=[number0,number1,number2,number3,number4,number5,number6,number7,number8,number9]; T = targets; %targets = eye(10); [R,Q] = size(P); S1 = 5; [S2,Q] = size(T); net = newff(minmax(alphabet),[S1 S2],{'logsig' 'logsig'},'traingdx'); net.LW{2,1} = net.LW{2,1}*0.01; net.b{2} = net.b{2}*0.01; % 无噪声字母识别网络的训练 net.performFcn = 'sse'; %执行函数为误差平方和函数 net.trainParam.epochs=800; %最大训练步长 net.trainParam.lr=0.01; %学习率 net.trainParam.lr_inc =1.05; %增长的学习率 net.trainParam.lr_dec =0.7; net.trainParam.goal=0.1; %执行函数目标值 net.trainParam.mc =0.9; %附加动量因子 net.trainParam.min_grad=1e-10; %最小执行梯度 net.trainParam.show = 50; P = alphabet; T = targets; [net,tr] = train(net,P,T); pause % 按任意键继续 % 具有噪声的输入矢量识别网络的训练 netn = net; netn.trainParam.goal = 0.1; %目标误差. netn.trainParam.epochs = 300; %最大训练步长. for pass=1:10 fprintf('Pass = %.0f\n',pass); T = [targets targets targets targets]; P = [alphabet, alphabet, ... (alphabet + randn(R,Q)*0.1), ... (alphabet + randn(R,Q)*0.2)]; [netn,tr] = train(netn,P,T); end pause % 按任意键继续 % 再次用无噪声字母训练识别网络 netn.trainParam.goal = 0.1; % 均方误差目标. netn.trainParam.epochs = 500; % 最大训练步长 net.trainParam.show = 5; % 训练中显示的频率 P = alphabet; T = targets; [netn,tr] = train(netn,P,T); pause % 按任意键测试所训练的网络性能 % 测试所训练的网络性能 % ==================== noise_range = 0:.02:0.2; max_test = 100; network1 = []; network2 = []; T = targets; % PERFORM THE TEST for noiselevel = noise_range fprintf('Testing networks with noise level of %.2f.\n',noiselevel); errors1 = 0; errors2 = 0; for i=1:max_test P = alphabet + randn(35,10)*noiselevel; % TEST NETWORK 1 A = sim(net,P); AA = compet(A); errors1 = errors1 + sum(sum(abs(AA-T)))/2; % fprintf('%.2f %.2f\n',AA,errors1); %fprintf('AA is %6.2f and errors1 is %8.3f \n', AA, errors1) % TEST NETWORK 2 An = sim(netn,P); AAn = compet(An); errors2 = errors2 + sum(sum(abs(AAn-T)))/2; echo off end % 100组10个矢量的平均误差 network1 = [network1 errors1/10/100]; network2 = [network2 errors2/10/100]; end echo on pause % 按任意键显示结果 % 显示结果 % ======== clf plot(noise_range,network1*100,'--',noise_range,network2*100); grid xlabel('Noise Level'); ylabel('Percentage of Recognition'); echo off %三种情况下数字"8"的表示情况(改变alphabet(:,9)中的数字9为8.7...可以看到不同的数字情况) %加入0.1的噪声 noisyY1 = alphabet(:,6)+randn(35,1) * 0.1; figure(2); plotchar(noisyY1); A2=sim(net,noisyY1); A2=compet(A2); answer=find(compet(A2)==1); figure(3); %加入0.1的噪声后的识别结果 plotchar(alphabet(:,answer)); %noiseY2=alphabet(:,10)+randn(35,1)*0.1; %figure(3); %plotchar(noiseY2); % %idealY=alphabet(:,10); % figure(4) % plotchar(idealY); %加入0.2的噪声 % noisyY11 = alphabet(:,6)+randn(35,1) * 0.2; % figure(4); % plotchar(noisyY1); % A21=sim(net,noisyY1); % A21=compet(A21); % answer=find(compet(A21)==1); % figure(5); % plotchar(alphabet(:,answer));