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

clear; 
clc; 
 
%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); 
I1 = load('E:\图像\神经网络\apen1.txt'); 
I2 = load('E:\图像\神经网络\kc1.txt'); 
I3 = load('E:\图像\神经网络\mir1.txt'); 
I = [I1 I2 I3]'; 
J1 = load('E:\图像\神经网络\apen2.txt'); 
J2 = load('E:\图像\神经网络\kc2.txt'); 
J3 = load('E:\图像\神经网络\mir2.txt'); 
J = [J1 J2 J3]'; 
     
for j=1:10 
    for i=1:80 
        % Input sample vectors2 
        s = size(I); 
        MinMaxVal = minmax(I); 
         
        % Test sample 
        I = [I1 I2 I3]'; 
        SimSample = I(:, i); 
         
        % Target vectors 
        TN = zeros(1, 40);   % 0代表正常 
        TF = ones(1, 40);    % 1代表脂肪 
        T = [TN TF]; 
              % Construct a feed-forward artificial neural network 
        net = newff(MinMaxVal, [10 1], {'logsig', 'logsig'}); 
        net.trainParam.show = 100; 
        net.trainParam.lr = 0.2; 
        net.trainParam.epochs = 1000; 
        net.trainParam.goal = 1e-5; 
         
        [net,tr]=train(net,I,T); 
    end 
     
    for nn=1:50 
    SimSample = J(:, nn); 
    SimResult(j,nn) = sim(net,SimSample); 
   end 
end 
 
 
 
 
save bpresult.mat  SimResult;