I am developing a simple backprop neural network with n inputs and 1 output. I am using a sigmoid activation function. [Aforge.Net]
I have read that it is good to normalise the input and output data prior to training, which I have done using a simple linear relation (max/min mapping) to normalise between [0, 1].
My question is, what happens when upon using the NN after training, I get inputs that are larger/smaller than the max/min of the training sets (used for normalisation)? Should I be storing new max/mins and using these to re-normalise my new inputs? or do I just normalise the data with the parameters used in the training set and simply give the network inputs that may be greater than 1 or less than 0?