A new learning rate based on Andrei method for training feed-forward artificial neural networks

Abstract

In this paper we developed a new method for computing learning rate for Back-propagation algorithm to train a feed-forward neural networks. Our idea is based on the approximating the inverse Hessian matrix for the error function originally suggested by Andrie. Experimental  results show that the proposed method considerably improve the convergence rate  of the  Back-propagation algorithm for the chosen test problem.