In previous papers, we presented an empirical methodology based on Neural Networks for obtaining fuzzy rules which allow a system to be described, using a set of examples with the corresponding inputs and outputs. Now that the previous results have been completed, we present another procedure for obtaining fuzzy rules, also based on Neural Networks with Backpropagation, with no need to establish beforehand the labels or values of the variables that govern the system.
@article{urn:eudml:doc:39089,
title = {Neural methods for obtaining fuzzy rules.},
journal = {Mathware and Soft Computing},
volume = {3},
year = {1996},
pages = {371-382},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:39089}
}
Benítez, José Manuel; Blanco, Armando; Delgado, Miguel; Requena, Ignacio. Neural methods for obtaining fuzzy rules.. Mathware and Soft Computing, Tome 3 (1996) pp. 371-382. http://gdmltest.u-ga.fr/item/urn:eudml:doc:39089/