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
From page
275
To page
280
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
Record number
2602171
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