Different methods for the fine-optimization of fuzzy-rule-based-systems

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 algorithm. Some possibilities in combining fuzzy-controllers with neural networks are the NFECON-model, the model of Lin and Lee or the model we propose: the MFOS (Munsteraner fuzzy optimization 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 deals with the basic algorithms of the MFOS and more detailed with the fine tuning of the fuzzy rules