Local Identification of Prototypes for Genetic Learning of Accurate TSK Fuzzy Rule-Based Systems
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AuthorAlcalá Fernández, Rafael; Alcalá Fernández, Jesús; Casillas Barranquero, Jorge; Cordón García, Óscar; Herrera Triguero, Francisco
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
SponsorshipCICYT Project TIC2002-04036-C05-01
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.