DOI : https://doi.org/10.18517/ijaseit.7.3.2074

The Effect of Pre-Processing Techniques and Optimal Parameters selection on Back Propagation Neural Networks

Nazri M Nawi (1), Ameer Saleh Hussein (2), Noor Azah Samsudin (3), Norhamreeza Abdul Hamid (4), Mohd Amin Mohd Yunus (5), Mohd Firdaus Ab Aziz (6)
(1) Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Johor, Malaysia
(2) Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Johor, Malaysia
(3) Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Johor, Malaysia
(4) Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Johor, Malaysia
(5) Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Johor, Malaysia
(6) Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Johor, Malaysia
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How to cite (IJASEIT) :
Nawi, Nazri M, et al. “The Effect of Pre-Processing Techniques and Optimal Parameters Selection on Back Propagation Neural Networks”. International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 3, June 2017, pp. 770-7, doi:10.18517/ijaseit.7.3.2074.
Citation Format :
The architecture of Artificial Neural Network laid the foundation as a powerful technique in handling problems such as pattern recognition and data analysis. Its data driven, self-adaptive, and non-linear capabilities channel it for use in processing at high speed and ability to learn the solution to a problem from a set of examples. Neural network training has been a dynamic area of research, with the Multi-Layer Perceptron (MLP) trained with Back Propagation (BP) mostly worked on by various researchers. In this study, a performance analysis based on BP training algorithms; gradient descent and gradient descent with momentum, both using the sigmoidal and hyperbolic tangent activation functions, coupled with pre-processing techniques are executed. The Min-Max, Z-Score, and Decimal Scaling pre-processing techniques are analyzed. Results generated from the simulations reveal that pre-processing the data greatly increase the ANN convergence, with Z-Score producing the overall best performance on all datasets
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