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Conjugate Gradient Back-propagation with Modified Polack –Rebier updates for training feed forward neural network | ||
| IRAQI JOURNAL OF STATISTICAL SCIENCES | ||
| Article 24, Volume 11, Issue 2, 2011, Pages 164-173 PDF (0 K) | ||
| DOI: 10.33899/iqjoss.2011.027897 | ||
| Authors | ||
| Abbas Al-Bayati; Ibrahem A. Saleh; Khalil K. Abbo | ||
| Abstract | ||
| Several learning algorithms for feed-forward (FFN) neural networks have been developed, many of these algorithms are based on the gradient descent algorithm well-known in optimization theory which have poor performance in practical applications. In this paper we modify the Polak-Ribier conjugate gradient method to train feed forward neural network. Our modification is based on the secant equation (Quasi-Newton condition). The suggested algorithm is tested on some well known test problems and compared with other algorithms in this field. | ||
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