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Author dela Ossa, L. ♦ Gámez, J.A. ♦ Puerta, J.M.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2010
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Pragmatics ♦ Search problems ♦ Prediction algorithms ♦ Construction industry ♦ Proposals ♦ Ant colony optimization ♦ Space exploration
Abstract The COR methodology allows the learning of Linguistic Fuzzy Rule-Based Systems by considering cooperation among rules. In order to do this, it uses search techniques, such as Genetic Algorithms, to find the set of candidate rules which will be used to build the final rule base. The performance of COR algorithms, in terms of the quality of the solutions and cost of the search, decreases as the problem size grows. In this paper, several local search algorithms for learning the rule base are tested, as an alternative to population-based methods. Experiments show that, in most cases, the results for the error of prediction improve upon those obtained with Genetic Algorithms. Moreover, this proposal allows a drastic reduction in the computational effort required to find the solutions.
Description Author affiliation: Department of Computer Systems, University of Castilla-La Mancha, Campus Universitario s/n, 02071 Albacete, Spain (dela Ossa, L.; Gámez, J.A.; Puerta, J.M.)
ISBN 9781424469192
ISSN 10987584
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2010-07-18
Publisher Place Spain
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781424469215
Size (in Bytes) 605.62 kB
Page Count 8
Starting Page 1
Ending Page 8


Source: IEEE Xplore Digital Library