Selection of fuzzy control rules using automatic tuning of membership functions

Author

Nishimori, Katsumi ; Hirakawa, Susumu ; Hiraga, Hirohito ; Ishihara, Naganori

Author_Institution

Dept. of Electr. & Electron. Eng., Tottori Univ., Japan

fYear

1994

fDate

18-21 Dec 1994

Firstpage

82

Lastpage

83

Abstract

Tuning of membership functions using learning procedure in a neuro-like approach has been developed to select fuzzy control rules. The tuning method is applied to simulation of driving control of a model car to run on a straight road. Simulation results bring out similar optimal trajectories of the car in both cases of 3×3 (=9 rules) and 7×7 (=49 rules) control rule types after tuning. Estimation function of errors used in the tuning of 3×3 rule type rapidly decreases to the convergent value of that used in 7×7 with increasing learning iteration

Keywords

automobiles; fuzzy control; learning systems; neural nets; automatic tuning; driving control; fuzzy control rules; learning iteration; learning procedure; membership functions; model car; optimal trajectories; Estimation error; Fuzzy control; Gravity; Kinetic theory; Neural networks; Optimal control; Optimization methods; Production; Roads; Wheels;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society Biannual Conference, 1994. Industrial Fuzzy Control and Intelligent Systems Conference, and the NASA Joint Technology Workshop on Neural Networks and Fuzzy Logic,

Conference_Location

San Antonio, TX

Print_ISBN

0-7803-2125-1

Type

conf

DOI

10.1109/IJCF.1994.375146

Filename

375146