How can I improve my neural network output?

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I have a prediction problem with 6x365 inputs( 6 represent, for example, the temperature for last six hours since now of every day from my database registrations) and 1x365 targets(I want to predict the next hour temperature). I don't how to choose the number of hidden layers, the number of nodes, the training algorithm, the transfer function.
That's my code now and the output is not very desirable:
net1 = newff(minmax(Input_temp),[20 1],{'tansig' 'purelin'},'traincgb','learngd');
net1.trainParam.epochs = 5000;
[net1,pr] = train(net1,Input_temp,Target_temp);
Output_temp = net1(Sample_temp)
Thank you for your time, Have a good day!

Accepted Answer

Greg Heath
Greg Heath on 8 May 2015
"Not very desirable" is not very helpful. Need a numerical measure. For example
NMSE = mse(target-output)/var(target,1) % Normalized mean-square-error
NEWFF with the (minmax ... syntax has been obsolete for 10 yrs or so.
NEWFF with the (input,target,... syntax has been obsolete for 5 yrs or so.
What version of the NNToolbox are you using? Do you have FITNET?
[I N ] = size(input) % [ 6 365 ]
[O N ] = size(target) % [ 1 365 ]
Ntrn = N-2*round(0.15*N) % 255 (default 70% training examples)
Ntrneq = Ntrn*O % 255 training equations
% Nw = (I+1)*H + (H+1)*O % Number of unknown weights
% Ntrneq > Nw <==> H <= Hub % "u"pper "b"ound
Hub = -1+ceil((Ntrneq-O)/(I+O+1)) % 31
Try 10 or more multiple designs with H as small as possible (H << Hub is highly desirable but not necessary). For each value of H create 10 designs with different random initial weights and data divisions.
For many, many examples search the NEWSGROUP and ANSWERS using
greg Hmin:dH:Hmax Ntrials
Hope this helps.
Thank you for formally accepting my answer

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