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An Evolutionary Artificial Neural Networks Approach for BF Hot Metal Silicon Content Prediction

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Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3610))

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Abstract

This paper presents an evolutionary artificial neural network (EANN) to the prediction of the BF hot metal silicon content. The pareto differential evolution (PDE) algorithm is used to optimize the connection weights and the network’s architecture (number of hidden nodes) simultaneously to improve the prediction precision. The application results show that the prediction of hot metal silicon content is successful. Data, used in this paper, were collected from No.1 BF at Laiwu Iron and Steel Group Co..

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References

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Author information

Authors and Affiliations

  1. Institute of System Optimum Technique, Zhejiang University, Hangzhou, 310027, China

    Zhao Min, Liu Xiang-guan & Luo Shi-hua

Authors
  1. Zhao Min
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  2. Liu Xiang-guan
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  3. Luo Shi-hua
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Min, Z., Xiang-guan, L., Shi-hua, L. (2005). An Evolutionary Artificial Neural Networks Approach for BF Hot Metal Silicon Content Prediction. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_46

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  • DOI: https://doi.org/10.1007/11539087_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28323-2

  • Online ISBN: 978-3-540-31853-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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