Fuzzy logic controller performance depends on the fuzzy control rule set. This set can be obtained either by an expert or from a learning algorithm through a set of examples. Recently, we have developed SLAVE an inductive learning algorithm capable of identifying fuzzy systems. The refinement of the rules proposed by SLAVE (or by an expert) can be very important in order to improve the accuracy of the model and in order to simplify the description of the system. The refinement algorithm is based on an heuristic process of generalization, specification, addition and elimination of rules.
@article{urn:eudml:doc:39133,
title = {Refinement of a fuzzy control rule set.},
journal = {Mathware and Soft Computing},
volume = {5},
year = {1998},
pages = {175-187},
zbl = {0969.68677},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:39133}
}
González, Antonio; Pérez, Raúl. Refinement of a fuzzy control rule set.. Mathware and Soft Computing, Tome 5 (1998) pp. 175-187. http://gdmltest.u-ga.fr/item/urn:eudml:doc:39133/