A Novel Model Based on PSO-GA-RBF Approach for Predicting Roll Gap During Unsteady Processes
Author:
Affiliation:
1. North China University of Science and Technology,Tangshan,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10485650/10485652/10485850.pdf?arnumber=10485850
Reference10 articles.
1. A novel forecast model based on CF-PSO-SVM approach for predicting the roll gap in acceleration and deceleration process
2. Application of machine learning methods for the prediction of roll force and torque during plate rolling of micro-alloyed steel[J];Kant;Journal of Alloys and Metallurgical Systems,2023
3. Modelling the slab stack shuffling problem in developing steel rolling schedules and its solution using improved Parallel Genetic Algorithms
4. Application of neural networks for predicting hot-rolled strip crown
5. Optimal Estimation and Feedforward Control of Strip-Longitudinal Hardness for Thickness Hunting Suppression of Tandem Cold Mill Process
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