Data-Based Modelling Techniques for Reciprocal Space of NMR Imaging

Abstract

In this paper the application of data-based modelling techniques to a reciprocal space components of a Nuclear Magnetic Resonance (NMR) irnaging system is considered. These include conventional identification techniques based on Auto-Regressive Moving Average with eXogenous inputs (ARMAX), Non-Linear ARMAX (NARMAX) and identifications based on Artificial Intelligence (AI) techniques such as Artificial Neural Networks (ANN). The mean sum of squared errors and graphical fit are used to compare the optimum performance of each technique