| In this paper we discuss a fuzzy classi er with pyramidal membership functions and discuss a performance improvement by changing linear membership functions to quadratic membership functions. First we de ne the classi er with quadratic membership functions and then discuss the training method that maximizes the recognition rate by counting the net increase in the recognition rate by changing ...

This paper generalizes the concepts of rough membership functions in pattern classification tasks to fuzz rough membership functions. Unlike the rough membersgp value of a pattern, which is sensitive only towards the rough uncertainty associated with the pattern, the fuzzy-rough membership value of the pattern signlfies the rou h uncertainty as well as the . fuzz uncertainty associated wig it. ...

In fuzzy systems, membership functions determine the groups to which a variable can belong to, and these are static or only have one setting in some aspect. However, systems typically require model dynamic environment they represent. Still, this behavior does not reflect conventional way. Thus, capable of reflecting dynamics real-time context. The approach presented consists system where transf...

The objective of this chapter is to provide a semantic framework for fuzzy sets in the theory of rough sets. Rough membership functions are viewed as a special type of fuzzy membership functions interpretable using conditional probabilities. The relationships between fuzzy membership functions and rough membership functions, between core and support of fuzzy set theory and lower and upper appro...

Many approaches have been proposed for mining fuzzy association rules. The membership functions, which critically influence the final mining results, are difficult to define. In general, multiple criteria are considered when defining membership functions. In this paper, a multi-objective genetic-fuzzy mining algorithm is proposed for extracting membership functions and association rules from qu...

Abstract: A simple Bayesian approach to nonparametric regression is described using fuzzy sets and membership functions. Membership functions are interpreted as likelihood functions for the unknown regression function, so that with the help of a reference prior they can be transformed to prior density functions. The unknown regression function is decomposed into wavelets and a hierarchical Baye...