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A Novel Prediction Scheme for Hot Rolled Strip Thickness Based on Extreme Learning Machine

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Cognitive Systems and Signal Processing (ICCSIP 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 710))

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Abstract

In order to predict the hot-rolled strip thickness, two extreme learning machine (ELM)-based thickness modeling algorithms based on clustering and differential evolution algorithm are proposed in this paper. These two kinds of modeling methods are used to predict the thickness, and the experimental results are compared with the standard ELM. The final results show that the two models proposed in this paper are better than the standard ELM model, and these two kinds of modeling methods can be selected according to different production conditions.

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References

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Author information

Authors and Affiliations

  1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Yonghong Xie, Jingyu Liu, Dezheng Zhang & Xiong Luo

  2. Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China

    Yonghong Xie, Jingyu Liu, Dezheng Zhang & Xiong Luo

Authors
  1. Yonghong Xie
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  2. Jingyu Liu
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  3. Dezheng Zhang
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  4. Xiong Luo
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Corresponding authors

Correspondence to Dezheng Zhang or Xiong Luo .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fuchun Sun

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Huaping Liu

  3. College of Mechatronics and Automation, National University of Defense Technology, Changsha, China

    Dewen Hu

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Xie, Y., Liu, J., Zhang, D., Luo, X. (2017). A Novel Prediction Scheme for Hot Rolled Strip Thickness Based on Extreme Learning Machine. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_18

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  • DOI: https://doi.org/10.1007/978-981-10-5230-9_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5229-3

  • Online ISBN: 978-981-10-5230-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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