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Conjugate Gradient Algorithm Based on Meyer Method for Training Artificial Neural Networks | ||
| IRAQI JOURNAL OF STATISTICAL SCIENCES | ||
| Article 17, Volume 13, Issue 3, 2013, Pages 118-132 PDF (0 K) | ||
| DOI: 10.33899/iqjoss.2013.081294 | ||
| Authors | ||
| Khalil K. Abbo; Hind H. Mohammed | ||
| Abstract | ||
| The difference between the desired output and the actual output of an multi-layers feed forward neural network produces an error value can be expressed it as a function of the network weights. Therefore training the network becomes an optimization problem to minimize the error function. This search suggests a new formula for computing learning rate based on Meyers formula to modify conjugate gradient algorithm (MCG) for training the FFNN. Typically this method accelerate the method of Fletcher–Reeves (FRCG) and Polak–Ribere (PRCG) when using it to solve three different types problems well known in the artificial neural network (namely, XOR problem, function approximation, and the Monk1 problem ) with 100 simulations. | ||
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