Neural Network Model of Stress State Coefficient of Medium and Heavy Plate Rolling Mill
[1]MENG Lingqi,WANG Jianxun,LEI Mingjie,et al.Neural Network Model of Stress State Coefficient of Medium and Heavy Plate Rolling Mill[J].Journal of Zhengzhou University (Engineering Science),2009,30(02):103-106.
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
30卷
Number of periods:
2009年02期
Page number:
103-106
Column:
Public date:
1900-01-01
- Title:
- Neural Network Model of Stress State Coefficient of Medium and Heavy Plate Rolling Mill
- Keywords:
- stress state coefficient; artificial neural networks; Medium and thick plate rolling mills
- CLC:
- -
- DOI:
- -
- Abstract:
- In order to seek a new method for calculating the stress state coefficient, based on a large number of measured data of 4200 rolling mill, using the Matlab neural network toolbox, taking the thickness of steel plates before and after rolling as input neurons, and taking the Qp obtained by measuring the rolling pressure and relying on the pressure formula for inverse operation as the output neurons, the BP neural network model and GRNN neural network model of the correspondence between the stress state coefficient of the rolling deformation zone and the thickness of the steel plate before and after rolling were established. The results show that it is feasible to predict the stress state coefficient by artificial neural network algorithm. The comparison between the GRNN neural network model and the BP model shows that the GRNN network has higher accuracy and stronger generalization ability.
- References:
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- Similar References:
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Last Update:
1900-01-01