Artificial Neural Networks: -A Panacea to Modelling Problems?

Hype has played a significant role in the history of artificial neural network research. From the initial recognition of the potential for networks to approximate non-linear functions some quite exaggerated claims have been an unwelcome accompaniment to research developments. Results from both simulation studies and real process applications indicated that even simple network architectures appeared to possess powerful non-linear process modelling capabilities that provided easy and quick practical solutions to complex problems Optimism grew and the pragmatic solution was borne. In contrast, the structure of a neural network based model was also being considered generic in the sense that little prior knowledge of the process was required. Unlike ARMAX and NARMAX approaches the methodology has been attributed the potential of accurately describing the behaviour of extremely complex systems. Claims that the technique offered a panacea to all modelling problems surfaced. This paper considers a number of case studies and examines the justification for these claims.