Local Identification of Prototypes for Genetic Learning of Accurate TSK Fuzzy Rule-Based Systems
Metadatos
Mostrar el registro completo del ítemAutor
Alcalá Fernández, Rafael; Alcalá Fernández, Jesús; Casillas Barranquero, Jorge; Cordón García, Óscar; Herrera Triguero, FranciscoEditorial
WILEY
Fecha
2007Referencia bibliográfica
Alcala, R., Alcala-Fdez, J., Casillas, J., Cordon, O., & Herrera, F. (2007). Local identification of prototypes for genetic learning of accurate TSK fuzzy rule-based systems. International Journal of Intelligent Systems, 22(9), 909-941. doi:10.1002/int.20232
Patrocinador
CICYT Project TIC2002-04036-C05-01Resumen
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.