A Learning Process for Fuzzy Control Rules using Genetic Algorithms (1995)
| Citations: | 32 - 22 self |
BibTeX
@TECHREPORT{Herrera95alearning,
author = {F. Herrera and F. Herrera and M. Lozano and M. Lozano and J. L. Verdegay and J. L. Verdegay},
title = {A Learning Process for Fuzzy Control Rules using Genetic Algorithms},
institution = {},
year = {1995}
}
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Abstract
The purpose of this paper is to present a genetic learning process for learning fuzzy control rules from examples. It is developed in three stages: the first one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy control rules, removing the redundant fuzzy rules, and the third one is a tuning process for adjusting the membership functions of the fuzzy rules. The three components of the learning process are developed formulating suitable Genetic Algorithms. Keywords: Fuzzy logic control systems, learning, genetic algorithms. 1 Introduction Fuzzy rule based systems have been shown to be an important tool for modelling complex systems, in which due to the complexity or the imprecision, classical tools are unsuccessful. Fuzzy Logic Controllers (FLCs) are now considered as one of the most important applications of the fuzzy rule based systems. The e...
