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Application of Adaptable Neural Networks for Rolling Force Set-Up in Optimization of Rolling Schedules

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

This paper presents two optimization procedures–single and multi objective optimization for 1370mm tandem cold rolling schedules, in which back propagation (BP) neural network is adopted to predict the rolling force instead of traditional models. Analysis and comparison with existing schedules are offered. The results show that the proposed schedules are more promising.

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References

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Author information

Authors and Affiliations

  1. Institute of Electrical Engineering, Yanshan University, Qinhuangdao, P.R. China

    Jingming Yang, Haijun Che, Yajie Xu & Fuping Dou

Authors
  1. Jingming Yang
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  2. Haijun Che
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  3. Yajie Xu
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  4. Fuping Dou
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Yang, J., Che, H., Xu, Y., Dou, F. (2006). Application of Adaptable Neural Networks for Rolling Force Set-Up in Optimization of Rolling Schedules. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_126

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  • DOI: https://doi.org/10.1007/11760191_126

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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