Modelling capabilities of neurofuzzy networks for nonlinear control

Neural and fuzzy algorithms have been introduced as basic adaptive nonlinear systems, therefore it is only natural that control engineers should investigate these models in order to determine whether or not they form a useful paradigm which can be applied in certain cases. They are not a universal panacea for all modelling and control problems and just because they learn to form nonlinear mappings does not necessarily mean that they will outperform more conventional nonlinear modelling techniques. From the engineering community, research should be aimed at establishing the modelling and learning abilities of these adaptive neural and fuzzy systems in order to determine whether they will form another methodology which can be applied to real-world problems; irrespective of the fact that these algorithm may have some vague biological relevance. In this paper the authors investigate the modelling and generalisation abilities of a certain class of neural algorithms called neurofuzzy networks