This paper deals with the problem of automatic rule generation for fuzzy systems design. The proposed approach is based on hybrid artificial bee colony (ABC) optimization and weighted least squares (LS) method and aims to find the structure and parameters of fuzzy systems simultaneously. More precisely, two ABC based fuzzy modeling strategies are presented and compared. The first strategy uses ...

We examine the classification performance of fuzzy rule-based systems designed by three-objective genetic rule selection. While a single rule set is usually obtained from a single run of rule generation methods, multiple rule sets are simultaneously obtained by a single run of our rule selection method with three objectives: to maximize the number of correctly classified training patterns, to m...

The static synchronous compensator (STATCOM) has recently received much attention owing to its ability to stabilize power systems and mitigate voltage variations. This paper investigates a novel interval type-II fuzzy rule-based PID (proportional-integral-derivative) controller for the STATCOM to mitigate bus voltage variations caused by large changes in load and the intermittent generation of ...

In this paper, we propose automatic generation methods of fuzzy classification rules with the Genetic Algorithms (GAs) to obtain compact fuzzy systems. This time, we propose an approach of hyper-cone membership function to construct rules for the antecedent part. Then, this method is determined the location and shape of hyper-cone membership function in the antecedent part, output class and the...

recently, tuning the weights of the rules in fuzzy rule-base classification systems is researched in order to improve the accuracy of classification. in this paper, a margin-based optimization model, inspired by support vector machine classifiers, is proposed to compute these fuzzy rule weights. this approach not only  considers both accuracy and generalization criteria in a single objective fu...

In this paper, a two-phase evolutionary optimization scheme is proposed for obtaining optimal structure of fuzzy control rules and their associated weights, using evolutionary programming (EP) and the principle of maximum entropy (PME) based on the previous research [1]. 1 Two-Phase Evolutionary Optimization A fuzzy logic controller (FLC) with weighted rules, which is equivalent to a convention...

One of the problems that focus the research in the linguistic fuzzy modeling area is the trade-off between interpretability and accuracy. To deal with this problem, different approaches can be found in the literature. Recently, a new linguistic rule representation model was presented to perform a genetic lateral tuning of membership functions. It is based on the linguistic 2-tuples representati...

Our study proposes an alternative method in building Fuzzy Rule-Based System (FRB) from Support Vector Machine (SVM). The first set of fuzzy IF-THEN rules is obtained through an equivalence of the SVM decision network and the zero-ordered Sugeno FRB type of the Adaptive Network Fuzzy Inference System (ANFIS). The second set of rules is generated by combining the first set based on strength of f...