Choosing Appropriate Neuro-Fuzzy Models

To use fuzzy controllers for automization tasks appropriate fuzzy sets and fuzzy rules have to be deened. This can be diicult in some domains, and the resulting controller has to be tuned. Neuro{fuzzy models can help in this tuning process by adapting fuzzy sets and creating fuzzy rules. Combinations of neural networks and fuzzy controllers are suitable if there is only partial knowledge in the form of fuzzy sets and fuzzy rules, but training data is available. To be be able to choose an appropriate model one has to know the diierent approaches to neural fuzzy control. In this paper we present a classiication of generic neural fuzzy controllers and give some hints when to choose a certain type of model.

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