W.-M. Lippe and S. Niendieck (Germany)
Fuzzy control, optimiziation, neural networks
Fuzzy controllers are widely used for control purposes. But fuzzy controllers are static, so it is not possible to adapt them or create them automatically. On the other side neural networks are well known for their capability of self-adapting and learning. Therefore it is helpful to represent a given fuzzy-controller by means of a neural network and to have the rules and / or the fuzzy sets adapted by special learning algorithms. Some possibilities in combining fuzzy-controllers with neural networks are the NEFCON-model, the model of Lin and Lee or the model we propose: the MFOS (M¨unsteraner fuzzy optimiziation system). The MFOS can represent nearly any given fuzzy controller, adapt the rules and fuzzy sets, and transform this optimized net back in a fuzzy-controller. This paper will deal with the basic algorithms of the MFOS and more detailled with the fine tuning of the fuzzy rules.
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