Structure organization of hierarchical fuzzy model using by genetic algorithm
Toshio Fukuda, Yasuhisa Hasegawa, Koji Shimojima · 2002
This paper proposes a method to organize hierarchical structure of fuzzy model using the genetic algorithm and backpropagation method. The number of fuzzy rules increases exponentially as the number of input variables increases. So the fuzzy system with many input variables has extremely large number of fuzzy rules. Hierarchical structure of fuzzy reasoning is one of the methods to reduce the number of fuzzy rules and membership functions. However the hierarchical structure cannot be made without considering the relationship among input and output variables. The proposed method can choose the valid input variables among input variables and organize the suitable hierarchical structure for the relationship among input and output variables. It is based on the genetic algorithm with an evaluation function as a strategy that adopts a system with fewer fuzzy rules and more accurate outputs. The proposed method is applied to the approximation problem of multidimensional nonlinear functions in order to show the effectiveness.>