On systolic general system solution for optimal artificial neural network training

Abstract

This paper presents systolic algorithms for general system solution and generalised matrix inversion in the context of the optimal linear associative memory artificial neural network. and with applications to optimised training strategies for two layer supervised learning feedforward artificial neural networks.