Methodological Identification of Information Granules-Based Fuzzy Systems by Means of Genetic Optimization

Author(s):  
Sung-Kwun Oh ◽  
Keon-Jun Park ◽  
Witold Pedrycz
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Leticia Cervantes ◽  
Oscar Castillo ◽  
Denisse Hidalgo ◽  
Ricardo Martinez-Soto

We propose to use an approach based on fuzzy logic for the adaptation of gap generation and mutation probability in a genetic algorithm. The performance of this method is presented with the benchmark problem of flight control and results show how it can decrease the error during the flight of an airplane using fuzzy logic for some parameters of the genetic algorithm. In this case of study, we use fuzzy systems for adapting two parameters of the genetic algorithm to improve the design of a type 2 fuzzy controller and enhance its performance to achieve flight control. Finally, a statistical test is presented to prove the performance enhancement in the application using fuzzy adaptation in the genetic algorithm. It is important to mention that not only is this idea for control problems but also it can be used in pattern recognition and many different problems.


Author(s):  
Itzel G. Gaytan-Reyes ◽  
Nohe R. Cazarez-Castro ◽  
Selene L. Cardenas-Maciel ◽  
David A. Lara-Ochoa ◽  
Armando Martinez-Graciliano

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