Fuzzy modeling by hierarchically built fuzzy rule bases

Fuzzy modeling by hierarchically built fuzzy rule bases

Although Mamdani-type fuzzy rule-based systems (FRBSs) became successfully performing clearly interpretable fuzzy models, they still have some lacks related to their accuracy when solving complex problems. A variant of these kinds of systems, which allows to perform a more accurate model representation, are the so-called approximate FRBSs. This alternative representation still cannot avoid the ...

Fuzzy system identification by generating and evolutionary optimizing fuzzy rule bases consisting of relevant fuzzy rules

One approach for system identification among many others is the fuzzy identification approach. The advantage of this approach compared to other analytical approaches is, that it is not necessary to make an assumption for the model to be used for the identification. In addition, the fuzzy approach can handle nonlinearities easier than analytical approaches. The Fuzzy–ROSA method is a method for ...

Fuzzy systems design principles - building fuzzy IF-THEN rule bases

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Enhanced Fuzzy Modeling Using Special Membership Functions and Fuzzy Rule Bases

A special fuzzy modeling method for developing multi-variable fuzzy models on the basis of measured input and output data is presented. Representing the crucial point in fuzzy modeling, the fuzzy model identiication procedure is carried out by applying a special clustering method, the fuzzy c-elliptotypes method, providing the parameters of the fuzzy model. To enhance the eeciency of the fuzzy ...

Constructing Heirarchical Fuzzy Rule Bases for Classification