How to scale data in a neural network?

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Hi,

I'm learning about neural networks. How do I scale data in a neural network multilayer backpropagation? I've found this formula for the input and test values:

I = Imin + (Imax-Imin)*(X-Dmin)/(Dmax-Dmin)

Input values are real numbers like in this multiplication table

1 1 1  (1 x 1 =1)
1 2 2  (1 x 2 =4)
.
.
.
2 3 6  (2 x 3 =6)
.
.
5 5 25 (5 x 5=25)

I'd like to know how do I unscale output data to get the real output answers?

Thank you


thanks to this answer I've started my scaling phase, though I'm search for the good normalization formulas.
One can find so many papers on nn but very few ones on scaling un-scaling your data.

I've started this coding from the article
Back-propagation Neural Net[^]
where someone has implemented the multiplication table solved by the backpropagation algorithm.

I keep on my search,
I'm using:

(double)LO+(HI-LO)*((X - minX)/(maxX - minX));

to scale, where:
LO=-1
HI=1
minX=1
maxX=25  (last result on my multiplication table)
X=input to scale

In need to find out how to un-scale and if I'm using the right scaling method

Well, thanks again