@misc{10481/64967,
year = {2007},
url = {http://hdl.handle.net/10481/64967},
abstract = {This work presents the use of local fuzzy prototypes as a new idea to obtain accurate local
semantics-based Takagi–Sugeno–Kang ~TSK! rules. This allow us to start from prototypes considering the interaction between input and output variables and taking into account the fuzzy
nature of the TSK rules. To do so, a two-stage evolutionary algorithm based on MOGUL
~a methodology to obtain Genetic Fuzzy Rule-Based Systems under the Iterative Rule Learning
approach! has been developed to consider the interaction between input and output variables.
The first stage performs a local identification of prototypes to obtain a set of initial local
semantics-based TSK rules, following the Iterative Rule Learning approach and based on an
evolutionary generation process within MOGUL ~taking as a base some initial linguistic fuzzy
partitions!. Because this generation method induces competition among the fuzzy rules, a postprocessing stage to improve the global system performance is needed. Two different processes
are considered at this stage, a genetic niching-based selection process to remove redundant rules
and a genetic tuning process to refine the fuzzy model parameters. The proposal has been tested
with two real-world problems, achieving good results.},
organization = {CICYT Project TIC2002-04036-C05-01},
publisher = {WILEY},
title = {Local Identification of Prototypes for Genetic Learning of Accurate TSK Fuzzy Rule-Based Systems},
doi = {10.1002/int.20232},
author = {Alcalá Fernández, Rafael and Alcalá Fernández, Jesús and Casillas Barranquero, Jorge and Cordón García, Óscar and Herrera Triguero, Francisco},
}