AWIT – Artificial Neural Networks What If Theory
Using an auto-encoder ANN we will approximate the implicit function of any dataset during the learning phase and to assign a fuzzy output to any new input during the recall phase.
It is in this sense that we define AWIT (ANN What If Theory) as the interpretation of a dataset (traditionally referred to as the Testing Dataset) using the logic present in another dataset (the Training Dataset).
The algorithm to implement this new approach to data analysis follows:
- Let DB1 and DB2 be two different datasets with the same types of variables but possessing different records;
- The function f() is a non-linear function optimally interpolating DB1, by means of an auto-encoder ANN consisting of one hidden layer;
- The dataset DB2 is rewritten using the ANN trained on the DB1dataset;
- The output represents how each variable of the DB2 dataset is reformulated using the “logic” of the DB1 dataset.
 P.M.Buscema, W.J.Tastle
Artificial Neural Network. What-If Theory
International Journal of Information Systems and Social Change, 6(4), 52-81, October-December 2015. July 2015.
 P.M. Buscema, G. Maurelli, F.S. Mennini, L. Gitto, S. Russo, M. Ruggeri, S. Coretti, A. Cicchetti
Artificial neural networks and their potentialities in analyzing budget health data: an application for Italy of what-if theory
in Quality & Quantity, 10.1007/s11135-016-0329-y, Springer Science+Business Media Dordrecht 2016. 24 March 2016.