Title of article :
Fast Training Algorithms for Feed Forward Neural Networks
Author/Authors :
Tawfiq, Luma N. M. University of Baghdad - College of Education for Pure Science (Ibn AL-Haitham) - Department of Mathematics, Iraq , Oraibi, Yaseen A. University of Baghdad - College of Education for Pure Science (Ibn AL-Haitham) - Department of Mathematics, Iraq
Abstract :
The aim of this paper, is to discuss several high performance training algorithms fall into two main categories. The first category uses heuristic techniques, which were developed from an analysis of the performance of the standard gradient descent algorithm. The second category of fast algorithms uses standard numerical optimization techniques such as: quasi-Newton . Other aim is to solve the drawbacks related with these training algorithms and propose an efficient training algorithm for FFNN.
Keywords :
Artificial neural network , Feed Forward neural network , Training Algorithm.
Journal title :
Ibn Alhaitham Journal For Pure and Applied Science
Journal title :
Ibn Alhaitham Journal For Pure and Applied Science
