I'm normalizing (or standardizing or feature scaling) my neural network training inputs and training targets. I just doing linear scaling and the formula I'm using is:
I = Imin + (Imax-Imin)*(D-Dmin)/(Dmax-Dmin)
I is the scaled input value,
Imax are the desired min and max range of the scaled values,
D is the original data value, and
Dmax are the min and max range of the original data values. In my case I'm setting
Imax to 1 and
Imin to -1.
I'm trying to predict a continuous real-valued output. How do I scale the output of my network back into the "unscaled" range?