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An Evolutionary Hybrid Model for the Prediction of Flow Stress of Steel

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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

A new hybrid model combining evolutionary artificial neural network (EANN) and mathematical models (MM) is proposed to improve the prediction precision of flow stress of 45 steel. In EANN, the optimal parameters are obtained by chaotic particle swarm optimization (CPSO) algorithm. CPSO adopts chaotic mapping and combines local search and global search, possessing high search efficiency and good performance. The results obtained from the computational study have shown that the proposed model can correctly recur to the flow stress in the sampled data and it can also predict well the non-sampled data. The efficiency and accuracy of the proposed model are demonstrated in comparison with the model combining BP networks with mathematical models (BPN-MM) used in much literature.

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Author information

Authors and Affiliations

  1. Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China

    Ai-ling Chen, Gen-ke Yang & Zhi-ming Wu

Authors
  1. Ai-ling Chen
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  2. Gen-ke Yang
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  3. Zhi-ming Wu
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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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Chen, Al., Yang, Gk., Wu, Zm. (2006). An Evolutionary Hybrid Model for the Prediction of Flow Stress of Steel. 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_129

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  • DOI: https://doi.org/10.1007/11760191_129

  • 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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