ON THE GREEDY RIDGE FUNCTION NURAL NETWORKS FOR APPROXIMATION MULTIDIMENSIONAL FUNCTIONS

Abstract

The aim of this paper is to approximate multidimensional function ƒ ϵC(RS) by developing a new type of Feed forward neural networks (FFNNs) which we called it Greedy ridge function neural networks (GRGFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy ridge function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Back propagation algorithm and the result in [1]).