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[4522] Rozdział: Neuro-fuzzy systems for rule-based modelling of dynamic processesw książce: Advances in Computational Intelligence and Learning, Methods and Applications ( H.-J. Zimmermann, G. Tselentis, M. van Someren, G. Dounias (Red.))ISBN: 0792376455 Wydawca: Kluwer Academic Publishers Opublikowano: 2002 Liczba stron: 12 Liczba arkuszy wydawniczych: 0.60 Autorzy / Redaktorzy / Twórcy Grupa MNiSW: Autorstwo rozdziału w monografii naukowej w językach: angielskim, niemieckim, francuskim, hiszpańskim, rosyjskim lub włoskim Punkty MNiSW: 3 Keywords: fuzzy sets  artificial neural networks  neuro-fuzzy systems  fuzzy modelling  fuzzy identification  |
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 of 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).