Mapping Some Functions and Four Arithmetic Operations to Multilayer Feedforward Neural Networks

This paper continues the development of a heuristic initialization methodology for designing multilayer feedforward neural networks aimed at modeling nonlinear functions for engineering mechanics applications as presented previously at IMAC XXIV and XXV. Seeking a transparent and domain knowledge-based approach for neural network initialization and result interpretation, this study examines the efficiency of linear sums of sigmoidal functions while offering constructive methods to approximate certain functions and operations. This effort directly contributes to the further extension of the proposed initialization procedure in that it opens the door for the approximation of a wider range of nonlinear functions.