www.gusucode.com > som-bp混合神经网络的matlab程序源码 > matlab_emulator/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;