Approximation and Fuzzy Rule Bases

Constructing Heirarchical Fuzzy Rule Bases for Classification

Annoyance Prediction with Fuzzy Rule Bases

This paper places the perception of noise annoyance within the framework of environmental assessment. It is shown that due to the inherent vagueness and uncertainties in the involved concepts, available data and knowledge, fuzzy techniques are very well suited for the modeling of this perception. This idea is then used to construct a fuzzy rule based model for the prediction of the level of gen...

Applying Boolean Transformations to Fuzzy Rule Bases

Neuro-fuzzy classi cation systems allow to derive fuzzy classi ers by learning from data. The obtained fuzzy rule bases are sometimes hard to interpret, even if the learning method uses constraints to ensure an appropriate fuzzy partitioning of the input domains. This paper describes an approach to build more expressive rules by performing boolean transformations during and after the learning p...

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 systems design principles - building fuzzy IF-THEN rule bases

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