Fuzzy Logic Controllers Lee90] (FLCs) are being widely and successfully applied in diierent areas. Fuzzy Logic Controllers can be considered as knowledge-based systems, incorporating human knowledge into their Knowledge Base through Fuzzy Rules and Fuzzy Membership Functions (among other information elements). The deenition of these Fuzzy Rules and Fuzzy Membership Functions is actually aaected...

Corresponding Author: Beatriz Wilges Department of Information Systems, Federal University of Santa Catarina, Florianópolis, Brazil Email: [email protected] Abstract: The ever-increasing amount of information on the Web is organized in structured, semi-structured and unstructured data. Text classification systems, capable of handling such different structures, may facilitate the work of im...

Linguistic modeling of complex irregular systems is helpful for the generation of decision making controls. In the various existing Fuzzy models, proposed by Mamdani, Sugeno, and Tsukamoto, the concepts of the set of membership functions and different Fuzzy logic rules to reason about data were addressed. The time control issues were not discussed in these models. In this paper, a new model is ...

This paper gives the structure optimization of fuzzy control systems based on genetic algorithm in the MATLAB environment. The genetic algorithm is a powerful tool for structure optimization of the fuzzy controllers, therefore, in this paper, integration and synthesis of fuzzy logic and genetic algorithm has been proposed. The genetic algorithms are applied for fuzzy rules set, scaling factors ...

A clonal selection algorithm (CLONALG) inspires from Clonal Selection Principle used to explain the basic features of an adaptive immune response to an antigenic stimulus. In this study, a new method is proposed for optimization of the Multiple Input Single Output (MISO) fuzzy membership functions using CLONALG. The most appropriate placement of membership functions with respect to fuzzy variab...

Fuzzy Logic Controllers are knowledge-based systems, incorporating human knowledge into their Knowledge Base through Fuzzy Rules and Fuzzy Membership Functions (among other information elements). The deenition of these Fuzzy Rules and Fuzzy Membership Functions is actually aaected by subjective decisions, having a great innuence over the performance of the Fuzzy Controller. This paper describes...

This paper presents an automatic learning algorithm which generates a fuzzy rule based knowledge representation. While learning the membership functions and rules the internal structure of the rule base is also considered. This is done by definition of 1 a complexity cost function and 2 a minimal Fuzzy System. A Genetic Algorithm is used to estimate the Fuzzy Systems which capture a low comlex ...

This paper presents a fuzzy goal programming (FGP) methodology for solving bi-level quadratic programming (BLQP) problems. In the FGP model formulation, firstly the objectives are transformed into fuzzy goals (membership functions) by means of assigning an aspiration level to each of them, and suitable membership function is defined for each objectives, and also the membership functions for vec...

Data mining is most commonly used in attempts to induce association rules from transaction data. Transactions in real-world applications, however, usually consist of quantitative values. This paper thus proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. We present a GA-based framework for finding membership funct...