The Relational Equations approach is one of the most usual ones for describing (Fuzzy) Systems and in most cases, it is the final expression for other descriptions. This is why the identification of Relational Equations from a set of examples has received considerable atention in the specialized literature. This paper is devoted to this topic, more specifically to the topic of max-min neural networks for identification. Three methods of learning Fuzzy Systems are developed by combining the most desirable properties of two existing ones: Sayto-Mukaidono's technique and the so called smoothed derivative technique.
@article{urn:eudml:doc:39031,
title = {Max-min fuzzy neural networks for solving relational equations.},
journal = {Mathware and Soft Computing},
volume = {1},
year = {1994},
pages = {335-345},
zbl = {0833.68111},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:39031}
}
Blanco, Armando; Delgado, Miguel; Requena, Ignacio. Max-min fuzzy neural networks for solving relational equations.. Mathware and Soft Computing, Tome 1 (1994) pp. 335-345. http://gdmltest.u-ga.fr/item/urn:eudml:doc:39031/