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New Scaled Conjugate Gradient Algorithm for Training Artificial Neural Networks Based on Pure Conjugacy Condition | ||
| Kirkuk Journal of Science | ||
| Article 1, Volume 10, Issue 3, 2015, Pages 230-241 PDF (0 K) | ||
| DOI: 10.32894/kujss.2015.104992 | ||
| Authors | ||
| Khalil K. Abbo; Hind H. Mohamed | ||
| Abstract | ||
| Conjugate gradient methods constitute excellent neural network training methods characterized by their simplicity efficiency and their very low memory requirements. In this paper, we propose a new scaled conjugate gradient neural network training algorithm which guarantees descent property with standard Wolfe condition. Encouraging numerical experiments verify that the proposed algorithm provides fast and stable convergence. | ||
| Keywords | ||
| Feed; forward Neural networks; training algorithms | ||
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