@misc{10481/77948,
year = {2012},
month = {4},
url = {https://hdl.handle.net/10481/77948},
abstract = {Fuzzy modelling research has traditionally focused on certain types of fuzzy rules. However, the use of
alternative rule models could improve the ability of fuzzy systems to represent a specific problem. In this
proposal, an extended fuzzy rule model, that can include relations between variables in the antecedent of
rules is presented. Furthermore, a learning algorithm based on the iterative genetic approach which is able
to represent the knowledge using this model is proposed as well. On the other hand, potential relations
among initial variables imply an exponential growth in the feasible rule search space. Consequently, two
filters for detecting relevant potential relations are added to the learning algorithm. These filters allows
to decrease the search space complexity and increase the algorithm efficiency. Finally, we also present an
experimental study to demonstrate the benefits of using fuzzy relational rules.},
organization = {P09-TIC-04813
I+D+i TIN2007-66367},
publisher = {Atlantis},
keywords = {Genetic fuzzy learning},
keywords = {Fuzzy Rules},
keywords = {Fuzzy relational rules},
keywords = {Classification},
keywords = {Inteligencia artificial},
keywords = {Artificial intelligence},
title = {An Efficient Inductive Genetic Learning Algorithm for Fuzzy Relational Rules},
doi = {10.1080/18756891.2012.685265},
author = {González Muñoz, Antonio and Pérez Rodríguez, Francisco G.Raúl and Caises, Yoel and Leyva, Enrique},
}