How can I predict new values in NARnet after it has been trained?

Hello! I would like to ask a few simple questions.

I have a 365x24 samples that I would like to use to train a NAR network. After that I want to use it to predict new 24 values with a 1x24 input data.

inPutss = xlsread('Datos','Hoja2');
targetSeries = tonndata(inPutss,false,false);
feedbackDelays = 1:2;
hiddenLayerSize = 10;
net = narnet(feedbackDelays,hiddenLayerSize);
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
[inputs,inputStates,layerStates,targets] = preparets(net,{},{},targetSeries);
net.divideFcn = 'divideblock'; 
net.divideMode = 'time';
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
net.trainFcn = 'trainlm'; 
[net,tr,YY,EE,XFF,AFF] = train(net,inputs,targets,inputStates,layerStates);
outputs = net(inputs,inputStates,layerStates);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs)
netc = closeloop(net);
[xc,xic,aic,tc] = preparets(netc,{},{},targetSeries);
yc = netc(xc,xic,aic);
perfc = perform(net,tc,yc)
nets = removedelay(net);
[xs,xis,ais,ts] = preparets(nets,{},{},targetSeries);
ys = nets(xs,xis,ais);
closedLoopPerformance = perform(net,tc,yc)

Okay now that I have my network trained, I want to use it to predict new values:

test = xlsread('Datos','Sheet1') %this is a 1x24 vector
test2 = tonndata(test,false,false)
sample1 = net(test2)
sample2 = netc(test2)
sample3 = nets(test3)

My first question is:

1- Which one of the three networks that the script created is the one that I should use to predict next-day values? In other words, which result is better? sample1, sample2 or sample3?

2- How can I add more layers (apart from the hidden ones) and how can I define the number of neurons inside each one of them?

Thanks to anyone that can answer my questions

Answer 1

[Greg Heath]

 inPutss = xlsread('Datos','Hoja2'); 
targetSeries = tonndata(inPutss,false,false); 
feedbackDelays = 1:2; 
hiddenLayerSize = 10; 
net = narnet(feedbackDelays,hiddenLayerSize); 
% net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'}; 
[inputs,inputStates,layerStates,targets] = preparets(net,{},{},targetSeries); 
net.divideFcn = 'divideblock'; 
% net.divideMode = 'time'; 
% net.divideParam.trainRatio = 70/100; 
% net.divideParam.valRatio = 15/100; 
% net.divideParam.testRatio = 15/100;
% net.trainFcn = 'trainlm'; 
[net,tr,YY,EE,XFF,AFF] = train(net,inputs,targets,inputStates,layerStates); 
% outputs = net(inputs,inputStates,layerStates); 
% errors = gsubtract(targets,outputs); 
% performance = perform(net,targets,outputs)
performance = perform(net,targets,YY)

'GEH1: NO PROOF THAT THE OL NET IS SUCCESSFUL' ; ' e.g., performance << var(cell2mat(transpose(targets)),1)' ;

 netc = closeloop(net); 
[xc,xic,aic,tc] = preparets(netc,{},{},targetSeries); 
yc = netc(xc,xic,aic); 
perfc = perform(net,tc,yc)

'GEH2: NO PROOF THAT THE CL NET IS SUCCESSFULL';

 nets = removedelay(net);

'GEH3: ERROR: CANNOT HAVE 0 FEEDBACK DELAY';

 [xs,xis,ais,ts] = preparets(nets,{},{},targetSeries); 
ys = nets(xs,xis,ais); 
closedLoopPerformance = perform(net,tc,yc)

'GEH4: TYPO: REPLACE net WITH nets ' ;

Okay now that I have my network trained, I want to use it to predict new values:

 test = xlsread('Datos','Sheet1') 
this is a 1x24 vector 
test2 = tonndata(test,false,false) 
sample1 = net(test2) 
sample2 = netc(test2)
sample3 = nets(test3)

' GEH5: ERROR : REPLACE test3 WITH test2 ';

 My first question is: 

 1- Which one of the three networks that the script created is the one that I should use to
predict next-day values? In other words, which result is better? sample1,sample2 or sample3?

' GEH6: NONE OF THE ABOVE. ';

' a. sample1 is openloop';

' b. sample3 cannot exist with with 0 feedback delay';

' c. sample2 has no specified Xic,Aic.';

 2- How can I add more layers (apart from the hidden ones) and how can I define the number of neurons inside each one of them?

'GEH7: net = narnet( FD, [H1 H2 H3])';

 Thanks to anyone that can answer my questions

Hope this helps.

Thank you for formally accepting my answer

Greg