Conjugate gradient back-propagation with modified Polack–Rebier updates for training feed forward neural network

Abstract EN

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.