The artificial neural network (ANN) has recently been applied in many areas, such as
medical, biology, financial, economy, engineering and so on. It is known as an excellent
classifier of nonlinear input and output numerical data. Improving training efficiency of
ANN based algorithm is an active area of research and numerous papers have been
reviewed in the literature. The performance of Multi-layer Perceptron (MLP) trained
with back-propagation artificial neural network (BP-ANN) method is highly influenced
by the size of the data-sets and the data-preprocessing techniques used. This work
analyzes the advantages of using pre-processing datasets using different techniques in
order to improve the ANN convergence. Specifically Min-Max, Z-Score and Decimal
Scaling Normalization preprocessing techniques were evaluated. The simulation results
showed that the computational efficiency of ANN training process is highly enhanced
when coupled with different preprocessing techniques
