Neural Network and Machine learning

Hello.

I have this code.

net = feedforwardnet(20); net.layers{1}.transferFcn = 'tansig'; net.layers{2}.transferFcn = 'purelin';

net.divideParam.trainRatio=.7; net.divideParam.valRatio=.15; net.divideParam.testRatio=.15; net.trainParam.max_fail = 500; net.trainParam.epochs = 500; net.trainParam.goal = .001;

i=0; fecha = datetime('now'); disp(fecha);

    Input =  xlsread('Datos',4);
    Target = xlsread('Datos',5);
    Sample = xlsread('Datos',6);

      for n = drange(1:4)
          i=i+1;
          disp(i);
      net=train(net,Input,Target);
     [net,tr] = train(net,Input,Target);
      end;

Y = net(Sample); Output = net(Target); y = net(Input); plotconfusion(Target,Output); Sample1 = sort(Sample,'descend'); plot(Sample1,'DisplayName','Modelo');hold on;plot(Sample,'DisplayName','Real');figure() [r,m,b] = regression(Output,y); plotregression(Target,Output); perf = perform(net,y,Target);

plotperform(tr); archivo = fopen('C:\Users\Pablo\Desktop\Neurona\Salida.txt','a'); fprintf(archivo,datestr(now, 'dd-mmm-yyyy\r\n')); fprintf(archivo,'%3.0f\r\n',Y); fclose(archivo); fprintf('%3.0f\n',round(Y)); fecha = datetime('now'); disp(fecha);

After traing and use the sim() I get a result, but How can a I make change a target using the same trained neuron ?? or a machine learning. Please some example to see ... Thank.

Answer 1

[Greg Heath]

1. Please format your code

2. Delete the 1st equation

    net     = train(net,Input,Target);
   [net,tr] = train(net,Input,Target);

3.The following is an error.(It doesn't make sense)

 Output = net(Target)

Hope this helps.

Thank you for formally accepting my answer

Greg

[ANDRES CONSIGLIO]

Hello Greg. Thank for your Answer, but I understand how can put a diffrent Target into the train NN. Can Do you make any example?? Thank again.

[Greg Heath]

A different target requires a different design.

The design is only valid for inputs in a restricted space.

For a different input in that space corresponding to a different target,

 output = net(input);

If you know the target you can calculate the error.

Hope this helps.

Greg