The proposed method develops a fuzzy rule-based classifier that was tested using features for islanding detection in distributed generation. In the developed technique, the initial classification boundaries are found out by using the decision tree (DT). From the DT classification boundaries, the fuzzy membership functions (MFs) are developed and the corresponding rule base is formulated for isl...

In most control and engineering applications, the use of fuzzy system as a way to improve the humtzn-computer interaction has becoming popular. This paper reports on the use of fuzzy system in mineral processing specifically in determining the parameter d50c of hydrocyclone. However, wit,h the inputoutput data provided to build the fuzzy rule base, it normally results in a sparse fuzzy rule bas...

An adaptive method to construct compact fuzzy systems for solving pattern classiication problems is presented. The method consists of two phases: a rule identiication phase and a rule selection phase. The rule identiication phase generates fuzzy rules from numerical data through a simple fuzzy grid method, then tunes the resulting fuzzy rules by training a neuro-fuzzy network used to model the ...

We examine several formulations of fuzzy rule selection for the design of fuzzy rulebased classification systems in our twostage approach. The first stage is heuristic rule extraction where a large number of candidate rules are extracted. The second stage is evolutionary rule selection where fuzzy rule-based systems are constructed by choosing a small number of candidate rules. Rule selection i...

1 This work is partially supported by the FCT Portuguese Foundation for Science and Technology under project POSI/SRI/47188/2002, and by Fundação ORIENTE – Portuguese Orient Foundation ABSTRACT: Fuzzy Causal Rule Bases (FCRb) are widely used and are the most important rule bases in Rule Based Fuzzy Cognitive Maps (RB-FCM) [1][4][5][6]. However, FCRb are subject to several restrictions that crea...

A fuzzy classifier system framework is proposed which employs a tree-based representation for fuzzy rule (classifier) antecedents and genetic programming for fuzzy rule discovery. Such a rule representation is employed because of the expressive power and generality it endows to individual rules. The framework proposes accuracy-based fitness for individual fuzzy classifiers and employs evolution...

Fuzzy association rule mining (Fuzzy ARM) uses fuzzy logic to generate interesting association rules. These association relationships can help in decision making for the solution of a given problem. Fuzzy ARM is a variant of classical association rule mining. Classical association rule mining uses the concept of crisp sets. Because of this reason classical association rule mining has several dr...