Fuzzy Logic Controllers may be considered as knowledge-based systems , incorporating human knowledge into their Knowledge Base through Fuzzy Rules and Fuzzy Membership Functions. 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. In recent years, eeorts have been mad...

In this contribution, we will recall graded fuzzy rules introduced in [6] and explain the difference from the classical fuzzy rules. Moreover, properties of formulae, which are used to formalize the graded fuzzy rules, will be analyzed.

This paper presents a layer-structured fuzzy neural network (FNN) for learning rules of fuzzy-logic control systems. Initially, FNN is constructed to contain all the possible fuzzy rules. We propose a two-phase learning procedure for this network. The first phase is a error-backprop (EBP) training, and the second phase is a rule pruning. Since some functions of the nodes in the FNN have the com...

This paper presents a fuzzy neural network system (FNNS) for implementing fuzzy inference systems. In the FNNS, a fuzzy similarity measure for fuzzy rules is proposed to eliminate redundant fuzzy logical rules, so that the number of rules in the resulting fuzzy inference system will be reduced. Moreover, a fuzzy similarity measure for fuzzy sets that indicates the degree to which two fuzzy sets...

We describe the basics of fuzzy sets and fuzzy logic. Based upon the concept of linguistic values, which describe imprecise concepts using words, the basics of fuzzy rules and fuzzy inference are introduced. In the second part we briefly explain applications of fuzzy rules for function approximation using fuzzy graphs, clustering using fuzzy algorithms, and classification under uncertainty usin...

Statistical process control ($SPC$), an internationally recognized technique for improving product quality and productivity, has been widely employed in various industries. $SPC$ relies on the use of control charts to monitor a manufacturing process for identifying causes of process variation and signaling the necessity of corrective action for the process. Fuzzy data exist ubiquitously in the ...

We discuss the problem of learning fuzzy rules using Evolutionary Learning techniques, such as Genetic Algorithms and Learning Classifier Systems. We present ELF, a system able to evolve a population of fuzzy rules to obtain a sub-optimal Fuzzy Logic Controller. ELF tackles some of the problems typical of the Evolutionary Learning approach: competition and cooperation between fuzzy rules, evolu...

The problem of generation desirable fuzzy rules is very important in the development of fuzzy systems. The purpose of this paper is to present a generation method of fuzzy control rules by learning from examples using genetic algorithms. We propose a real coded genetic algorithm for learning fuzzy rules, and an iterative process for obtaining a set of rules which covers the examples set with a ...