Structure Organization of Hierarchical Fuzzy Model Using Genetic Algorithm
Toshio Fukuda, Yasuhisa Hasegawa and Koji Shimojima
Dept. of Micro System Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, 464-01 Japan
Received:November 28, 1994Accepted:December 10, 1994Published:February 20, 1995
Keywords:Fuzzy model, Hierarchical structure, Genetic algorithm, Back-propagation method
This paper proposes a method to organize the hierarchical structure of fuzzy model using the Genetic Algorithm and back-propagation method. The number of fuzzy rules increases exponentially with the number of input variables. Thus, a fuzzy system with many input variables has an 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, it is very difficult to organize the hierarchical structure because the hierarchical structure cannot be constructed without considering the relationship among input and output variables. The proposed method can organize the suitable hierarchical structure for the relationship among input and output variables in teaching numerical data. 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 problems of multi-dimensional nonlinear functions in order to demonstrate its effectiveness.
Cite this article as:T. Fukuda, Y. Hasegawa, and K. Shimojima, “Structure Organization of Hierarchical Fuzzy Model Using Genetic Algorithm,” J. Robot. Mechatron., Vol.7 No.1, pp. 29-35, 1995.Data files: