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Journal of Central South University of Technology

, Volume 15, Issue 1, pp 136–140 | Cite as

Application of neural network to prediction of plate finish cooling temperature

  • Wang Bing-xing  (췵뇻탋)Email author
  • Zhang Dian-hua  (헅뗮뮪)
  • Wang Jun  (췵 뻽)
  • Yu Ming  (폚 쏷)
  • Zhou Na  (훜 쓈)
  • Cao Guang-ming  (닜맢쏷)
Article

Abstract

To improve the deficiency of the control system of finish cooling temperature (FCT), a new model developed from a combination of a multilayer perception neural network as the self-learning system and traditional mathematical model were brought forward to predict the plate FCT. The relationship between the self-learning factor of heat transfer coefficient and its influencing parameters such as plate thickness, start cooling temperature, was investigated. Simulative calculation indicates that the deficiency of FCT control system is overcome completely, the accuracy of FCT is obviously improved and the difference between the calculated and target FCT is controlled between −15 °C and 15 °C.

Key words

plate heat transfer coefficient mathematical model back propagation (BP) neural network 

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Copyright information

© Published by: Central South University Press, Sole distributor outside Mainland China: Springer 2008

Authors and Affiliations

  • Wang Bing-xing  (췵뇻탋)
    • 1
    Email author
  • Zhang Dian-hua  (헅뗮뮪)
    • 1
  • Wang Jun  (췵 뻽)
    • 1
  • Yu Ming  (폚 쏷)
    • 1
  • Zhou Na  (훜 쓈)
    • 1
  • Cao Guang-ming  (닜맢쏷)
    • 1
  1. 1.State Key Laboratory of Rolling and AutomationNortheastern UniversityShenyangChina

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