Abstract
Fuzzy controllers are designed to work with knowledge in the form of linguistic control rules. But the translation of these linguistic rules into the framework of fuzzy set theory depends on the choice of certain parameters, for which no formal method is known. The optimization of these parameters can be carried out by neural networks, which are designed to learn from training data, but which are in general not able to profit from structural knowledge. In this paper we discuss approaches which combine fuzzy controllers and neural networks, and present our own hybrid architecture where principles from fuzzy control theory and from neural networks are integrated into one system.
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Nauck, D., Klawonn, F., Kruse, R. (1993). Combining neural networks and fuzzy controllers. In: Klement, E.P., Slany, W. (eds) Fuzzy Logic in Artificial Intelligence. FLAI 1993. Lecture Notes in Computer Science, vol 695. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56920-0_6
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DOI: https://doi.org/10.1007/3-540-56920-0_6
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