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SLAVE (Structural Learning Algorithm in Vague Environment) is an inductive learning algorithm that uses concepts based on fuzzy logic theory. This theory has shown be an useful representation tool for improving the understanding under a human point of view, of the knowledge obtained. Furthermore, SLAVE uses an iterative approach for learning with genetic(More)
The completeness and consistency conditions were introduced in order to achieve acceptable concept recognition rules. In real problems, we can handle noise-aaected examples and it is not always possible to maintain both conditions. Moreover, when we use fuzzy information there is a partial matching between examples and rules, therefore the consistency(More)
Learning algorithms can obtain very useful descriptions of several problems. Many diierent alternative descriptions can be generated. In many cases, a simple description is preferable since it has a higher possibility of being valid in unseen cases and also it is usually easier to understand by a human expert. Thus, the main idea of this paper is to propose(More)
—SLAVE is an inductive learning algorithm that uses concepts based on fuzzy logic theory. This theory has been shown to be a useful representational tool for improving the understanding of the knowledge obtained from a human point of view. Furthermore, SLAVE uses an iterative approach for learning based on the use of a genetic algorithm (GA) as a search(More)
Genetic algorithms offer a powerful search method for a variety of learning tasks, and there are different approaches in which they have been applied to learning processes. Structural learning algorithm on vague environment (SLAVE) is a genetic learning algorithm that uses the iterative approach to learn fuzzy rules. SLAVE can select the relevant features(More)
This paper presents the use of genetic algorithms to develop smartly tuned fuzzy logic controllers dedicated to the control of heating, ventilating and air conditioning systems concerning energy performance and indoor comfort requirements. This problem has some specific restrictions that make it very particular and complex because of the large time(More)