Fuzzy and neural networks controller

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 sho...

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Bibliographic Details
Published inProceedings IECON '91: 1991 International Conference on Industrial Electronics, Control and Instrumentation pp. 1437 - 1442 vol.2
Main Authors Strefezza, M., Dote, Y.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1991
Subjects
Computer simulation
Control systems
Fuzzy control
Fuzzy logic
Fuzzy neural networks
Fuzzy reasoning
Fuzzy sets
Humans
Mathematical model
Neural networks
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Summary: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.< >
ISBN:9780879426880
0879426888
DOI:10.1109/IECON.1991.239131