NDLI: Genetic Learning of the Knowledge Base of a Fuzzy System by Using the Linguistic 2-Tuples Representation

Content Provider	IEEE Xplore Digital Library
Author	Alcala, R. Alcala-Fdez, J. Herrera, F. Otero, J.
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci. & Artificial Intelligence, Granada Univ. (Alcala, R.; Alcala-Fdez, J.; Herrera, F.)
Abstract	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 improving the accuracy of linguistic fuzzy modeling have been considered in the specialized literature, maintaining the desired trade-off between accuracy and interpretability. Recently, a new linguistic rule representation model was presented to perform a genetic lateral tuning of membership functions. It is based on the linguistic 2-tuples representation model that allows the lateral displacement of a label considering a unique parameter. It involves a reduction of the search space that eases the derivation of optimal models. Based on the linguistic 2-tuples representation model, in this work, we present a new method to obtain linguistic fuzzy systems by means of an evolutionary learning of the data base a priori (granularity and lateral displacements of the membership functions) and on the use of a basic rule generation method to obtain the whole knowledge base. In this way, the search space reduction provided by the linguistic 2-tuples representation helps to the evolutionary search technique to obtain more precise and compact knowledge bases. Moreover, we analyze this approach considering 21 real-world problems
Sponsorship	IEEE IEEE Neural Networks Soc
Starting Page	797
Ending Page	802
File Size	1993879
Page Count	6
File Format	PDF
ISBN	0780391594
DOI	10.1109/FUZZY.2005.1452496
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-05-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetics Fuzzy systems Computer science Learning Artificial intelligence Buildings Humans Fuzzy reasoning Shape Knowledge based systems
Content Type	Text
Resource Type	Article

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