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This paper evaluates the performance of ten significance measures applied to the problem of determining an appropriate network structure, for data modelling with neurofuzzy systems. The advantages of Neurofuzzy systems are demonstrated with application to both real and synthetic data interpretation problems.
Neurofuzzy systems have been developed as grey box modelling technique ideal for the task of system identiication. Neurofuzzy models combine the mathematical structure of Associative Memory Networks (AMNs) with the transparency of fuzzy systems. This produces a modelling technique to which mathematical analysis can be applied, while being more transparent… (More)
A desirable property of any empirical model is the ability to generalise well throughout the models input space. Recent work has seen the development of neurofuzzy model construction algorithms which identify neurofuzzy models from available empirical data and expert knowledge. By matching the models structure to the underlying process represented by the… (More)