Neuro-fuzzy tension controller for tandem rolling

Author

Janabi-Sharifi, F. ; Liu, J.

Author_Institution

Dept. of Mech. Aerosp. & Ind. Eng., Ryerson Univ., Toronto, Ont., Canada

fYear

2002

fDate

2002

Firstpage

309

Lastpage

314

Abstract

A fuzzy logic controller (FLC) is designed to maintain constant tension for tandem rolling mills. By envisioning the fuzzy inference system as a neural network and introducing a tutor, a backward propagation algorithm is used as a self-organization technique for the FLC to approach the best parameters under supervision. Simulation results exhibit the generalization and adaptivity of the neuro-fuzzy controller in offline tuning.

Keywords

backpropagation; fuzzy control; fuzzy neural nets; generalisation (artificial intelligence); neurocontrollers; nonlinear control systems; rolling mills; strain control; adaptivity; backward propagation algorithm; complex nonlinear rolling process; constant tension; fuzzy inference system; fuzzy logic controller; generalization; neural network; neuro-fuzzy tension controller; offline tuning; self-organization technique; simulation results; tandem rolling mills; Artificial neural networks; Automatic speech recognition; Control systems; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Humans; Milling machines; Resonance; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control, 2002. Proceedings of the 2002 IEEE International Symposium on

ISSN

2158-9860

Print_ISBN

0-7803-7620-X

Type

conf

DOI

10.1109/ISIC.2002.1157781

Filename

1157781