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A neural network based methodology for the prediction of roll force and roll torque in fuzzy form for cold flat rolling process.
- Source :
- International Journal of Advanced Manufacturing Technology; Dec2003, Vol. 22 Issue 11/12, p883-889, 7p
- Publication Year :
- 2003
-
Abstract
- Neural network models can be effectively used to predict any type of functional relationship. In this paper, a neural network model is used to predict roll force and roll torque in a cold flat rolling process, as a function of various process parameters. A strategy is developed to obtain a prescribed accuracy of prediction with a minimum number of data for training and testing. The effect of increasing the size of training and testing data set is also examined. After the prediction of most likely value, upper and lower bound estimates are also found with the help of the neural network. With these estimates, the predicted value can be represented as a fuzzy number for use in fuzzy-logic based systems. [ABSTRACT FROM AUTHOR]
- Subjects :
- ARTIFICIAL neural networks
TORQUE
METHODOLOGY
TESTING
FUZZY numbers
ESTIMATION theory
Subjects
Details
- Language :
- English
- ISSN :
- 02683768
- Volume :
- 22
- Issue :
- 11/12
- Database :
- Complementary Index
- Journal :
- International Journal of Advanced Manufacturing Technology
- Publication Type :
- Academic Journal
- Accession number :
- 16717753
- Full Text :
- https://doi.org/10.1007/s00170-003-1628-8