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
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;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1713479
