An integrated model of rolling force for extra-thick plate by combining theoretical model and neural network model

2022 ◽  
Vol 75 ◽  
pp. 100-109
Author(s):  
Shun Hu Zhang ◽  
Lei Deng ◽  
Li Zhi Che
Keyword(s):  
Neural Network ◽  
Theoretical Model ◽  
Network Model ◽  
Neural Network Model ◽  
Thick Plate ◽  
Rolling Force ◽  
Integrated Model

scholarly journals Modelling of Deformation Resistance with Big Data and Its Application in the Prediction of Rolling Force of Thick Plate

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shun Hu Zhang ◽  
Li Zhi Che ◽  
Xin Ying Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Rolling Force ◽  
Deformation Resistance ◽  
Force Model ◽  
The Neural Network ◽  
Bp Neural Network Model

The precision of traditional deformation resistance model is limited, which leads to the inaccuracy of the existing rolling force model. In this paper, the back propagation (BP) neural network model was established according to the industrial big data to accurately predict the deformation resistance. Then, a new rolling force model was established by using the BP neural network model. During the establishment of the neural network model, the data set of deformation resistance was established, which was calculated back from the actual rolling force data. Based on the data set after normalization, the BP neural network model of deformation resistance was established through the optimization of algorithm and network structure. It is shown that both the prediction accuracy of the neural network model on the training set and the test set are high, indicating that the generalization ability of the model is strong. The neural network model of the deformation resistance is compared with the theoretical one, and the maximum error is only 3.96%. Furthermore, by comparison with the traditional rolling force model, it is found that the prediction accuracy of the rolling force model imbedding with the present neural network model is improved obviously. The maximum error of the present rolling force model is just 3.86%. The research in this paper provides a new way to improve the prediction accuracy of rolling force model.

A neural network model of entropy discrimination in pigeons

2003 ◽  
Author(s):  
Christopher Silansky ◽  
Anthony Chemero
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model

A neural network model of pre-pulse inhibition

2003 ◽  
Author(s):  
Nestor Schmajuk ◽  
Roger Smith
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model
Sign in / Sign up

Export Citation Format

Share Document