Title :
Fuzzy and neural networks controller
Author :
Strefezza, M. ; Dote, Y.
Author_Institution :
Dept. of Electron. Eng., Muroran Inst. of Technol., Japan
fDate :
28 Oct-1 Nov 1991
Abstract :
The authors propose the use of back-propagation to produce a fuzzy controller. In this case two kinds of neural networks are trained: the first kind uses simple numerical data to obtain the membership functions, and the second kind is trained with 0s and 1s to obtain the fuzzy rules. The results show that it is possible to obtain a fuzzy controller without too much data to train the nets. Computer simulations were carried out. The controller was used to control the position of a DC motor. The results show a fast response of the motor without overshoot
Keywords :
control system synthesis; fuzzy logic; neural nets; DC motor; back-propagation; fuzzy controller; fuzzy rules; membership functions; neural networks controller; position control; Computer simulation; Control systems; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy reasoning; Fuzzy sets; Humans; Mathematical model; Neural networks;
Conference_Titel :
Industrial Electronics, Control and Instrumentation, 1991. Proceedings. IECON '91., 1991 International Conference on
Conference_Location :
Kobe
Print_ISBN :
0-87942-688-8
DOI :
10.1109/IECON.1991.239131
