hi, mahdi you can normalize any data before feeding them to the network by using mapminmax command...it normalizes data so that they would fall in the range of -1 & 1(defaults values)...to do this follow the steps below:
>> i_p_data=[4.04174435161 0.00572348 -0.0153630110827 0.602838009364 -2.36559733245;
3.03056109866 0.0662695 -0.0533391771878 0.55923481044 -1.91116449019;
2.52071323312 0.121233 0.260306969065 0.467349509343 -1.70735726533]; % i just put your data in a vector and gave it a name for simplicity.
>> [nomalized_i_p,ps1]=mapminmax(i_p_data);
>> test_data=[2.33842672041 -0.137656 -0.0597465187944 0.448352030836 -1.00431677993;
3.96620615784 0.0438668 0.219515113659 0.596424503954 -2.0851352013;
1.99407739363 0.100512 0.360384341206 0.385154801184 -1.18594026984];
>>[normalized_test,ps2]=mapminmax(test_data);
>> however, if you want to set them back to their original values i.e. denormalize them :
>> training_data=mapminmax('reverse',normalized_i_p,'ps1');
>> test_data1=mapminmax('reverse',normalized_test,'ps2');
you might also, want to check this for further details
i hope this was helpful.
yours,
hoda
