A comparison of neural networks and fuzzy relational systems in dynamic modelling

Both neural networks and fuzzy relational models show great potential for modelling poorly understood and highly nonlinear systems. Recent papers have suggested that both techniques could be used to form the model in model-based controller designs. In order to compare the two techniques, the well known Box-Jenkins furnace data were used. The software used to generate the neural networks was ´Neural-Works Explorer´, and in-house software was used to generate the fuzzy relational models. The predictive performance of both methods was compared on the dataset for a variety of model configurations. The paper describes the results of these tests and discusses the effects of changing model parameters on predictive and practical performance. For the neural models the factors investigated were: network configuration, transfer function type, and various combinations of learning schemes. The factors considered for the fuzzy relational models were the model structure and reference set definitions. As well as predictive performance criteria, practical modelling criteria such as modelling time, sensitivity, etc., were also used to compare the two methods.