Abstract
The aim of this paper is to present and compare four different neuro-fuzzy approaches to the construction of fuzzy rule-based models for dynamic processes. These approaches have been applied to modelling an industrial gas furnace system (Box-Jenkins benchmark). The following neuro-fuzzy systems have been considered: nfMod — the system proposed in this paper, the well-known ANFIS and NFIDENT systems, and an alternative neuro-fuzzy system reported in literature. The main criterion of comparison of all systems is their performance (the accuracy of modelling) versus interpretability (the transparency and the ability to explain generated decisions; it also includes an analysis and pruning of obtained fuzzy-rule bases).
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Gorzalczany, M.B., Gluszek, A. (2002). Neuro-Fuzzy Systems for Rule-Based Modelling of Dynamic Processes. In: Zimmermann, HJ., Tselentis, G., van Someren, M., Dounias, G. (eds) Advances in Computational Intelligence and Learning. International Series in Intelligent Technologies, vol 18. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0324-7_9
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DOI: https://doi.org/10.1007/978-94-010-0324-7_9
Publisher Name: Springer, Dordrecht
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