This paper introduces a recursive rule base adjustment to enhance the performance of fuzzy logic controllers. Here the fuzzy controller is constructed on the basis of a decision table (DT), relying on membership functions and fuzzy rules that incorporate heuristic knowledge and operator experience. If the controller performance is not satisfactory, it has previously been suggested that the rule...

In this paper, we examine and compare the performance of four fuzzy rule generation methods on Wisconsin breast cancer data [2]. These methods were reported by Ishibuchi [1] et al. For the diagnosis of breast cancer, the determination of the presence of benign/malignant breast tumors represents a very complex problem (even for an experienced cytologist). The goal of this paper is to compare and...

A rule selection scheme of evolutionary algorithm is proposed to design fuzzy path planner for shooting ability in robot soccer. The fuzzy logic is good for the system that works with ambiguous information. Evolutionary algorithm is employed to deal with difficulty and tediousness in deriving fuzzy control rules. Generic evolutionary algorithm, however, evaluate and select chromosomes which may...

In this contribution, we propose a new method to automatically learn the Knowledge Base of a Fuzzy Rule-Based System by ®nding an appropriate Data Base using a Genetic Algorithm and considering a simple generation method to derive the Rule Base. Our genetic process learns all the components of the Data Base (number of labels, working ranges and membership function shapes for each linguistic var...

In this paper a novel algorithm is proposed that integrates the process of fuzzy hierarchy generation and rule discovery for automated discovery of Production Rules with Fuzzy Hierarchy (PRFH) in large databases. A concept of frequency matrix (Freq) introduced to summarize large database that helps in minimizing the number of database accesses, identification and removal of irrelevant attribute...

This paper discusses inference strategies for fuzzy rule bases resulting from databased automatic rule generation algorithms. Typical methods for rule generation are tree-oriented, statistical and evolutionary approaches. The aim of these data-based methods is the design of compact rule bases with a small number of interpretable rules which map the learning data set and provide a su cient stati...

This work presents the use of local fuzzy prototypes as a new idea to obtain accurate local semantics-based Takagi–Sugeno–Kang ~TSK! rules. This allow us to start from prototypes considering the interaction between input and output variables and taking into account the fuzzy nature of the TSK rules. To do so, a two-stage evolutionary algorithm based on MOGUL ~a methodology to obtain Genetic Fuz...

In this paper we present a new fuzzy language } FOHDEL } for the syntactic description of handwritten symbols. The presented language incorporates the fuzzy logic techniques to describe the syntactic relations of the semantic features extracted from a symbol pattern. A FOHDEL rule-base represents the compact feature information extracted from a small number of character prototypes and covers va...

This paper focuses on the data-driven generation of fuzzy IF...THEN rules. The resulted fuzzy rule base can be applied to build a classifier, a model used for prediction, or it can be applied to form a decision support system. Among the wide range of possible approaches, the decision tree and the association rule based algorithms are overviewed, and two new approaches are presented based on the...

The chapter introduces a simple learning methodology, the cooperative rules (COR) one, that improves the accuracy of linguistic fuzzy models preserving the highest interpretability. Its operation mode involves a combinatorial search of fuzzy rules performed over a set of previously generated candidate ones. The accuracy is achieved by developing a smart search space reduction and by inducing th...