DocumentCode
2740173
Title
Optimization Design of Rolling Schedules with Rolling Force Self-learning
Author
Yang, Jingming ; Xu, Yajie ; Che, Haijun
Author_Institution
Inst. of Electr. Eng., Yanshan Univ., Hebei
Volume
2
fYear
0
fDate
0-0 0
Firstpage
7761
Lastpage
7765
Abstract
Single and multi-object optimization planning are presented for 1370mm tandem cold rolling schedules separately, in which, BP neural network with self-learning function is adopted to predict the rolling force instead of traditional models. Analysis and comparison with existing schedules are offered, and the performance of the optimal rolling schedules is satisfying
Keywords
backpropagation; cold rolling; neural nets; optimisation; unsupervised learning; backpropagation neural network; dynamic programming; multiobject optimization planning; rolling force self-learning; single optimization planning; tandem cold rolling schedule; Automation; Design optimization; Dynamic programming; Dynamic scheduling; Electronic mail; Gold; Intelligent control; Neural networks; Performance analysis; Predictive models; dynamic programming; neural network; optimize; rolling schedules; tandem cold rolling;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location
Dalian
Print_ISBN
1-4244-0332-4
Type
conf
DOI
10.1109/WCICA.2006.1713479
Filename
1713479
Link To Document