Neural Network - inverted pre-processing functions

I know that using preprocessing functions have to transform the output of the neural network to their original values using:

    P = inputs;
    t = targets;
    [pn, Mins, maxp, tn, mint, maxt] = premnmx (p, t);
    net = train (net, pn, tn);
    an = sim (net, pn);
    postmnmx a = (an, mint, maxt);

I would like to know if using fitnet with new preprocessing functions integrated into the neural network ('mapminmax', 'processpca', 'mapstd') for the training and calculating the output of a new data set using:

newoutputs = net (newinputs);

I still have to convert and reconvert the new data or the transformation is performed automatically.

Is there any difference if I use:

new outputs = net (newinputs);

instead of:

newoutputs = sim (net, newinputs);

?

Thank you!

Answer 1

[Greg Heath]

Why are you wasting time on a question that you can answer yourself by simply running the example in

 help fitnet

To better answer your problem add

 max(abs(t-y))

Hope this helps.

Greg

[Emiliano Rosso]

SOLVED

clear all
clearvars -global

inputs=[1,2,4,3,5;8,3,5,4,3];
targets=[1,0,0,1,1];
pnew=[1,4,3;1,6,5];

net=fitnet(1);

net.inputs{1}.processFcns = {};
net.outputs{2}.processFcns = {};

[pn,meanp,stdp,tn,meant,stdt] = prestd(inputs,targets);
net=train(net,pn,tn);

an = sim(net,pn); 
output1 = poststd(an,meant,stdt);

pnewn = trastd(pnew,meanp,stdp); 
anewn = sim(net,pnewn); 
output1_new = poststd(anewn,meant,stdt);
my_weights = getx(net);

save('output1');
save('output1_new');
save('my_weights');

clear all
clearvars -global

load('output1','output1');
load('output1_new','output1_new');
load('my_weights','my_weights');

inputs=[1,2,4,3,5;8,3,5,4,3];
targets=[1,0,0,1,1];
pnew=[1,4,3;1,6,5];

net=fitnet(1);
net.inputs{1}.processFcns = {};
net.outputs{2}.processFcns = {};
net.inputs{1}.processFcns = {'mapstd'};
net.outputs{2}.processFcns = {'mapstd'};

net=train(net,inputs,targets);
net = setx(net,my_weights);

output2=net(inputs);
output2_new=net(pnew);

save('output2');
save('output2_new');

clear all;
clearvars -global;

load('output1','output1');
load('output1_new','output1_new');
load('my_weights','my_weights');
load('output2','output2');
load('output2_new','output2_new');

if isequal(output1,output2)==1

      disp('output1 is equal to output2');
  else 
      disp('output1 is NOT equal to output2');
  end

if isequal(output1_new,output2_new)==1

      disp('output1_new is equal to output2_new');
  else
      disp('output1_new is NOT equal to output2_new');
  end

COMMAND WINDOW:

output1 is equal to output2
output1_new is equal to output2_new

getx & setx are used to use the same training.