www.pudn.com > SOM_Bp_HybridNetwork_matlab_emulator.rar > BP_ANN.m

 
% use the som to discern the  
%Initialize training samples 
 
 
% TN = zeros(1, length(normalroi1)); 
% TF = ones(1, length(fattyroi1)); 
% T = [TN TF]; 
%  
% net = newff(minmax(I), [5 1], {'logsig', 'logsig'}); 
% net.trainParam.show = 100; 
% net.trainParam.lr = 0.2; 
% % net.trainParam.mr = 0.9; 
% net.trainParam.epochs = 2000; 
% net.trainParam.goal = 1e-5; 
%  
% [net,tr]=train(net,I,T); 
% SimResult = sim(net,I); 
 
% --------------------- initial the BP train and test sample 
 
 
 
BP_train_T = [ones(1,train_positive_num),zeros(1,train_negative_num)];         %  train object 
BP_test_T = [ones(1,test_positive_num),zeros(1,test_negative_num)];         %  test object 
 
MinMaxValue = [zeros(BP_input_num ,1),ones(BP_input_num ,1)];        
BP_net = newff(MinMaxValue,[5,5,5,1],{'logsig','logsig','logsig','logsig'}); 
BP_net.trainParam.epochs = BP_epochs; 
BP_net.trainParam.goal = 1e-8; 
 
% ---------------------  training ----------------------- 
[BP_net,BP_tr] = train(BP_net,BP_train_P,BP_train_T); 
 
 
threshold = 0.5; 
%------------------------- cal the train mse 
A = sim(BP_net,BP_train_P); 
E = BP_train_T -A; 
Train_MSE = mse(E); 
 
train_False_positive = 0; 
for(j=1:train_positive_num) 
    if(A(j)<=threshold) 
        train_False_positive = train_False_positive + 1; 
    end 
end 
train_False_negative = 0; 
for(j=train_positive_num+1:train_positive_num + train_negative_num) 
    if(A(j)>threshold) 
        train_False_negative = train_False_negative + 1; 
    end 
end 
BPresult_train = A;      
%------------------------ cal the test mse ------------------ 
A = sim(BP_net,BP_test_P); 
E = BP_test_T - A; 
Test_MSE = mse(E); 
 
test_False_positive = 0; 
for(j=1:test_positive_num) 
    if(A(j)<=threshold) 
        test_False_positive = test_False_positive + 1; 
    end 
end 
test_False_negative = 0; 
for(j=test_positive_num+1:test_positive_num + test_negative_num) 
    if(A(j)>threshold) 
        test_False_negative = test_False_negative + 1; 
    end 
end 
BPresult_test = A; 
%--------------------------- save the result ----------------- 
savePath1 = 'E:\SOMBP\software\HybridNet004\'; 
file = '.mat'; 
savePath2 = [savePath1 'BP' file]; 
save(savePath2,'BPresult_train','BPresult_test',...... 
               'train_False_positive','train_False_negative',...... 
               'test_False_positive','test_False_negative',...... 
               'BP_net','BP_tr');  
            
% plot the result 
figure; 
title('BP train result'); 
for(i=1:train_positive_num) 
    hold on 
    plot(BPresult_train(i),0,'r*'); 
end 
for(i=train_positive_num+1:train_positive_num+train_negative_num) 
    hold on 
    plot(BPresult_train(i),0,'bsquare'); 
end 
 
figure; 
title('BP test result'); 
for(i=1:test_positive_num) 
    hold on 
    plot(BPresult_test(i),0,'r*'); 
end 
for(i=test_positive_num+1:test_positive_num+test_negative_num) 
    hold on 
    plot(BPresult_test(i),0,'bsquare'); 
end 
 
 minmax(BPresult_test(1:test_positive_num)) 
 minmax(BPresult_test(test_positive_num+1:test_positive_num+test_negative_num)) 
 bp_result = [train_False_positive,train_False_negative,test_False_positive,test_False_negative]