Improvement of Shape Recognition Performance of Sendzimir Mill Control Systems Using Echo State Neural Networks

作     者：Jung-hyun PARK Seong-ik HAN Jong-shik KIM 

作者机构：School of Mechanical EngineeringPusan National University School of Electrical EngineeringPusan National University 

出 版 物：《Journal of Iron and Steel Research(International)》 (钢铁研究学报（英文版）)

年 卷 期：2014年第21卷第3期

页      面：321-327页

核心收录：

学科分类：080503[工学-材料加工工程] 0806[工学-冶金工程] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0703[理学-化学] 0802[工学-机械工程] 0801[工学-力学（可授工学、理学学位）] 0702[理学-物理学] 080201[工学-机械制造及其自动化] 

基　　金：Sponsored by Korea Science and Engineering Foundation(KOSEF) Funded by Korea Government(MEST)(2010-0022521) 

主　　题：Sendzimir mill neural network multi-layer perceptron echo state network shape recognition 

摘      要：High rigidity twenty-high Sendzimir mills （ZRMs） are widely used for rolling stainless steels, silicon sheets, etc. A ZRM uses a small diameter work roll to produce massive rolling forces. Since a work roll with a small diameter can be bent easily, strips often have complex shapes with mixed quarter and deep edge waves in the shape of plates. In order to solve this problem, fuzzy neural network controls are generally used for shape： recognition in ZRM control systems. Among various neural network types, the multi-layer perceptron （MLP） is typically used in current ZRMs. However, an MLP causes the loss of a large amount of shape recognition data. To improve the shape recognition per- formance of ZRM control systems, echo state networks （ESNs） are proposed to be used. Through simulation re- sults, it is found that shape recognition performance could be improved using the proposed ESN method.