# Revert minmax normalization to original value

I'm training a neural network. Normalization of inputs and outputs (training data) is carried out using min and max to a scale of [0-1].

I'm applying backpropagation learning algorithm. I need to get the error offset. i.e. error = actual output $-$ output

How do I scale my output [0-1] back to actual real values such as in zero to thousands range?

output_ab = output_01 * (b - a) + a,
value_ab = ((value_xy - x) / (y - x)) * (b - a) + a.