In this paper, the method of least squares is applied to the fuzzy inference rules. We begin studying the conditions in which from a fuzzy set we can build another through the method of least squares. Then we apply this technique in order to evaluate the conclusions of the generalized modus ponens. We present different theorems and examples that demonstrate the fundamental advantages of the method studied.
@article{urn:eudml:doc:39130,
title = {Fuzzy inference using a least square model.},
journal = {Mathware and Soft Computing},
volume = {5},
year = {1998},
pages = {141-149},
zbl = {0957.68110},
mrnumber = {MR1704062},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:39130}
}
Bustince, Humberto; Calderón, M.; Mohedano, Victoria. Fuzzy inference using a least square model.. Mathware and Soft Computing, Tome 5 (1998) pp. 141-149. http://gdmltest.u-ga.fr/item/urn:eudml:doc:39130/