Why is normalization needed?
In order for a neural network to function properly, the values of the input data and the desired output data must fall within the range of the processing element transfer functions. For TanhAxons this range is between -1 and +1 and for SigmoidAxons the range is between 0 and 1. Q: Do I need to handle the normalization when building a neural network? The wizards automatically configure the File components to normalize (i.e., transform) the data between the bounds of the transfer functions. They also configure the output probes to denormalize (i.e., reverse the transform of) the output data back to the range of the desired output file. There is usually no need to make additional changes, but these can be made after the network is built. Q: Why does my network output fall outside the range of my desired output? The NeuralBuilder configures the File components to normalize the data between the range of -0.9 and 0.9 (for TanhAxons). The networks tend to learn a little better when they dont
