Minimization algorithm for training feed forward neural network

Abstract EN

In this paper we suggested a new learning rate  , which improves the classical Backpropagation algorithm (BP).

The derivatation of  are based on approximating the error function E to the quadratic one in sufficiently small neighborhood for the optimal weight vector.

The suggested algorithm (Spectral Backpropagation SBP say) is tested and the experimental results show that the SBP learning strategy improves the considered methods.