In this paper, a methodology to obtain a set of fuzzy rules for classification systems is presented. The system is represented in a layered fuzzy network, in which the links from input to hidden nodes represents the antecedents of the rules, and the consequents are represented by links from hidden to output nodes. Specific genetic algorithms are used in two phases to extract the rules. In the first phase an initial version of the rules is extracted, and in second one, the labels are refined. The procedure is illustrated by applying it to two real-world classification problems.
@article{urn:eudml:doc:39198,
title = {A methodology for constructing fuzzy rule-based classification systems.},
journal = {Mathware and Soft Computing},
volume = {7},
year = {2000},
pages = {185-197},
zbl = {0995.68606},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:39198}
}
Fernández Garrido, José María; Requena Ramos, Ignacio. A methodology for constructing fuzzy rule-based classification systems.. Mathware and Soft Computing, Tome 7 (2000) pp. 185-197. http://gdmltest.u-ga.fr/item/urn:eudml:doc:39198/