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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.
Modelling has become an invaluable tool in many areas of research, particularly in the control community where it is termed system identification. System identification is the process of identifying a model of an unknown process, for the purpose of predicting and/or gaining an insight into the behaviour of the process. Due to the inherent complexity of many… (More)
Neurofuzzy systems have been developed as grey box modelling technique ideal for the task of system identi cation. 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)