The Essential Order of (L_p,p<1) Approximation Using Regular Neural Networks

Abstract

This paper is concerning with essential degree of approximation using regular neural networks and how a multivariate function in L_p (K) spaces for p<1 can be approximated using a forward regular neural network. So, we can have the essential approximation ability of a multivariate function in L_p (K) spaces for p<1 using regular FFN