Fuzzy modeling has become one of the most productive and successful results of fuzzy logic.
Among others, it has been applied to knowledge discovery, automatic classification, long-…

Intelligent systems have particular potentialities and strengths to support decisional
situations faced by companies, especially those of a strategic nature, where good strategic …

Tuning fuzzy rule-based systems for linguistic fuzzy modeling is an interesting and widely
developed task. It involves adjusting some of the components of the knowledge base without …

Fuzzy modeling usually comes with two contradictory requirements: interpretability, which is
the capability to express the real system behavior in a comprehensible way, and accuracy, …

Abstract System modeling with fuzzy rule-based systems (FRBSs), ie fuzzy modeling (FM),
usually comes with two contradictory requirements in the obtained model: the interpretability, …

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 …

In this paper, we propose the use of weighted linguistic fuzzy rules in combination with a
rule selection process to develop accurate fuzzy logic controllers dedicated to the intelligent …

The inductive learning of a fuzzy rule-based classification system (FRBCS) is made difficult
by the presence of a large number of features that increases the dimensionality of the …

Nowadays society is deeply affected by web content. A web site, regardless of its category,
can provide or not for users their needs. To identify its strengths and weaknesses, a process …

One of the problems associated to linguistic fuzzy modeling is its lack of accuracy when
modeling some complex systems. To overcome this problem, many different possibilities of …