Fuzzy rule based classification systems is one of the most popular in pattern classification problems. The rules in the fuzzy models can be weighted to show the importance of generated rules where all attributes in the antecedent part of the rules have been usually weighted equally. Whereas the contributed attributes in a fuzzy model may have different influences on the decision making, a new m...

A new method for color image segmentation using fuzzy logic is proposed in this paper. Our aim here is to automatically produce a fuzzy system for color classification and image segmentation with least number of rules and minimum error rate. Particle swarm optimization is a sub class of evolutionary algorithms that has been inspired from social behavior of fishes, bees, birds, etc, that live to...

A fuzzy rough set approach has been proposed without using any fuzzy logical connectives. By this approach, gradual decision rules are induced from a given decision table. Fuzzy-rough modus ponens and fuzzy-rough modus tollens have been formulated based on the gradual decision rules. The equivalence condition between fuzzy-rough modus ponens and fuzzy-rough modus tollens is discussed. The condi...

Several approaches generalizing crisp association rules to fuzzy association rules have been proposed. In an our previous paper we introduced a pair of confidence measures for crisp association rules from which one can be obtained the majority known quality measures. In this paper, starting from these results we give an extension to fuzzy association rules.

Fuzzy rule-based systems have been very popular in many engineering applications. In mineral engineering, fuzzy rules are normally constructed using some fuzzy rule extraction techniques to establish the determination model in predicting the d50c of hydrocyclones. However, when generating fuzzy rules from the available information, it may result in a sparse fuzzy rule base. The use of more than...

This article proposes an algorithm for data mining that presents a new measure for assistance in the extraction of knowledge. The algorithm uses association rules to extract rules from the databases and fuzzy logic for the classification and comparison of the collected rules. Key-words: data mining, association rules, fuzzy logic, similarity and algorithm of the inverse confidence.

Many real world systems and applications must deal with imprecise or vague data. For such systems, information management components are needed that provide support for managing this imprecise data. Fuzzy theory allows us to model imprecise or vague data. The use of fuzzy theory also allows us to model vague knowledge. There have been several proposals for extending relational database systems ...

Original scientific paper A Fuzzy Skinner Operant Conditioning Automaton (FSOCA) is constructed based on Operant Conditioning Mechanism with Fuzzy Set theory. The main character of FSOCA automaton is: the fuzzed results of state by Gaussian function are used as fuzzy state sets; the fuzzy mapping rules of fuzzyconditioning-operation replace the stochastic "conditioning-operant" mapping sets. So...

This paper introduces a complete framework of Modified Adaptive Fuzzy Inference Engine (MAFIE) and its application. The fuzzy with hybridization schemes has become of research interest in versatile applications over the past decade. The fuzzy hybridizations models are quite popular among practitioners or researchers in various advanced promising fields to help solve problems with a small number...