The goal of this work is to study, expand and evaluate the use of multiobjective genetic algorithms and the iterative rule learning approach in fuzzy system generation, especially, in fuzzy rule-based systems, both in ...

The construction of the knowledge base of fuzzy systems has been beneficited intensively from automatic methods that extract the necessary knowledge from data sets which represent examples of the problem. The evolutionary ...

The objective of this work is to study, expand and evaluate the use of interval type-2 fuzzy sets in the knowledge representation for fuzzy inference systems, specifically for fuzzy classifiers, as well as its automatic ...

The process of building the knowledge base of fuzzy systems has benefited extensively of methods to automatically extract the necessary knowledge from data sets that represent examples of the problem. Among the topics ...

There is a number of frameworks for the general task of classification available for free usage on the Internet. However, software to generate fuzzy classification systems using the genetic approach is scarce. In this work, ...

This paper introduces a method for the supervision and control of devices in electric substations using fuzzy logic and artificial neural networks. An automatic knowledge acquisition process is included which allows the ...

The use of fuzzy logic and fuzzy sets theory applied to decision making process was initially proposed by Bellman and Zadeh (1970). Since then, the arisen of unstructured problems associated with multi-criteria characteristics ...

This paper introduces a method for the supervision and control of devices in electric substations using fuzzy logic and artificial neural networks. An automatic knowledge acquisition process is included which allows the ...

The success of Fuzzy Rule Based Systems is due to their ability to represent vague and uncertain knowledge and to the facility to express the system behavior in a language easily interpretable by human beings. The knowledge ...