Here's a process to reverse the effects of normalizing. The key point is that the normalize operator produces a model that can be applied to an unseen example set. This is important when making the attribute ranges the same in training and test data.
The De-Normalize operator takes a normalized model as input and reverses it so that when this is applied to a normalized example set, a de-normalized version is produced.
In the process, the result is the iris data set which is identical to the original.