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 ...

Association rule mining is an important data mining technique used for discovering relationships among all data items. Membership functions have a significant impact on the outcome of the mining association rules. An ...

In Computer Science, ontologies are used for knowledge representation in a number of applications, aiming to structure and handle domain semantics through models shared by humans and computational systems. Although traditional ...

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 ...

In the medical field, experts’ knowledge is based on experience, theoretical knowledge and rules. Case-based reasoning is a problem-solving paradigm which is based on past experiences. For this purpose, a large number of ...

Inductive Decision Trees (DT) are mechanisms based on the symbolic paradigm of machine learning which main characteristics are easy interpretability and low computational cost. Though they are widely used, the DTs can ...

The purpose of this paper is to introduce a new approach for edge detection in gray shaded images. The proposed approach is based on the fuzzy number theory. The idea is to deal with the uncertainties concerning the gray ...

In this article, the fuzzy Lyapunov function approach is considered for stabilising continuous-time Takagi-Sugeno fuzzy systems. Previous linear matrix inequality (LMI) stability conditions are relaxed by exploring further ...

In this article, the fuzzy Lyapunov function approach is considered for stabilising continuous-time Takagi-Sugeno fuzzy systems. Previous linear matrix inequality (LMI) stability conditions are relaxed by exploring further ...