How to convert standardized outputs of a neural network back to original scale?
Assuming the mean and standard deviation of the targets are mu and sigma, the normalized value of a target y should be (y-mu)/sigma. In that case if you get an output y', you can move it back to original scale by converting y' -> mu + y' * sigma.
In my neural network, I have inputs varying from 0 to 719, and targets varying from 0 to 1340. So, I standardize the inputs and targets by standard scaling such that the mean is 0 and variance is 1. Now, I calculate the outputs using back-propagation. All my outputs lie between -2 and 2. How do I convert these outputs to the original scale, i.e. lying in the range (0,1340)?
EDIT: I have 1 input, 5 hidden neurons and 1 output. I have used logistic sigmoid activation function. I did the standard scaling by taking mean and then dividing by standard deviation. In particular, my output lies between -1.28 and 1.64.