MATLAB: Not getting good results while simulation in NNTOOL

Deep Learning Toolboxneural network

hello sir, i am using nntool of matlab for my prediction my application is to forecast water demand based on previous consumption. year month day net supply 2003 1 1 2900 2003 1 1 2915 . .
      2010       12         31      3400.
my input matrix has 3 inputs month day and date and output matrix is simply net supply.i have trained my network using
nntool i got good results in training,validation and testing.ie(0.93).but when i try to simulate the network using nntool to forecast the consumption 
for 2011 year i am not getting good results.
i mean if the actual consumption on 1st jan 2011 is 3400 i am getting it as 2900.
can you give me any sugession how to improve the results.

Best Answer

continued ...
 You should scale and center the data.  zero-mean/unit-variance inputs  with tansig hidden node activation units are recommended..
 help prestd
 doc prestd
 similarly for trastd and poststd.
 >%creating network
 Initialize the uniform random number generator RAND so that 
 you can duplicate the results if necessary.
 > net=newff(minmax(x),[3,12,1],{'logsig','logsig','purelin'},'trainlm');
 Do not include the number of input nodes. That info is obtained 
 from minmax(x)
 >net.performFcn = 'mse';
 Delete. It is a default value.
 >net.trainParam.goal = 1e-4;
 Use a better informed choice that takes the data scaling into account..
 >net.trainParam.min_grad = 1e-20;
 >net.trainParam.epochs = 5000;
 Where did you get these values?
 Use as many defaults of newff  as possible.
 help newff
 doc newff
 >net=init(net);
 Delete. NEWFF is self-initializing.
 >net=train(net,x,y);
 >z=sim(net,x);
 Use the multiple output option.
 >test=sim(net,a);
 %%%%%%%%%%%%%%%%%%%%%%
 MSEtrn00    = mse(y-mean(y))    % Naive Constant Model
 MSEgoal      = MSEtrn00/100           % R^2 >= 0.99
 state0      = 0                             % Any integer < 2^32-1
 rand('state',state0)                     % help RAND
 net = newff(minmax(x),[12 3]);
 net.trainParam.goal = MSEgoal;
 [ net tr Y E] = train(net,x,y);       % help TRAIN, help TRAINLM
 MSEtrn = tr.perf(end)                 % = mse(y-Y)
 R2trn    = 1-MSEtrn/MSEtrn00
 MSEtst00 = mse(b-mean(b))
 tst             = sim(net,a);                   % test is not a good name
 MSEtst      = mse(b-tst);
 R2tst         = 1-MSEtst/MSEtst00
 % %evaluating errors
 -----SNIP
 >i tried with 10-30 neurons but the result is not good 
 >can you tell where its going wrong
 Implement a double loop over choice of H and creation of a 
 number of nets for each value of H.
 Hope this helps.
 Greg
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