Abstract
Fuzzy controller generating procedures when using crisp input-output data produce the necessary system in two steps: first they produce a starting rule set and then they tune the parameters that influence the approximation with a learning algorithm. Other solutions work under special conditions as hybrid neuro-fuzzy systems improving the approximation with a gradient based learning algorithm (e.g. in the case of monotonous membership functions), or use the methods of the genetic algorithms to generate the fuzzy controller. This article demonstrates a new method which reduces the problem to a classification task and carries out the generation of the rules and the tuning of the system in a single step.
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© 1999 Springer-Verlag Berlin Heidelberg
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Borgulya, I. (1999). Fuzzy Controller Generation with a Fuzzy Classification Method. In: Reusch, B. (eds) Computational Intelligence. Fuzzy Days 1999. Lecture Notes in Computer Science, vol 1625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48774-3_9
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DOI: https://doi.org/10.1007/3-540-48774-3_9
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