by Rafał Scherer
Abstract:
In the paper relational neuro-fuzzy systems are described with additional fuzzy relation connecting input and output linguistic fuzzy terms. Thanks to this the fuzzy rules have more complicated structure and can be better suited the task. Fuzzy clustering and relational equations are used to obtain the initial set of fuzzy rules and systems are then learned by the backpropagation algorithm. Simulations shows excellent performance of the modified neuro-fuzzy systems.
Reference:
R. Scherer, "Regression Modeling with Fuzzy Relations", Lecture Notes in Artificial Intelligence, vol. 5097, 2008, pp. 317-323.
Bibtex Entry:
@ARTICLE{SchererICAISC2008,
author = {Rafał Scherer},
title = {Regression Modeling with Fuzzy Relations},
journal = {Lecture Notes in Artificial Intelligence},
year = {2008},
volume = {5097},
pages = {317-323},
abstract = {In the paper relational neuro-fuzzy systems are described with additional
fuzzy relation connecting input and output linguistic fuzzy terms.
Thanks to this the fuzzy rules have more complicated structure and
can be better suited the task. Fuzzy clustering and relational equations
are used to obtain the initial set of fuzzy rules and systems are
then learned by the backpropagation algorithm. Simulations shows
excellent performance of the modified neuro-fuzzy systems.},
publisher = {Springer-Verlag},
url = {http://www.springerlink.com/content/g3t8l0v513t4284w/}
}