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Neural Networks Based Multiplex Forecasting System of the End-Point of Copper Blow Period

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3498))

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Abstract

The neural network and the experiential evaluation method are introduced into the industrial converting process forecast, and a multiplex forecast system is proposed at the end-point of copper blow period in a matte converting process. The fuzzy clustering analysis method is used to identify the number of hidden nodes. Results show that the forecast is highly dependable and capable of providing effective guidance to the production process.

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Author information

Authors and Affiliations

  1. Key Lab. for Precision and Non-traditional Machining Technol. of Ministry of Education, Dalian University of Technology, Dalian, Liaoning, 116023, China

    Lihua Xue & Hongzhong Huang

  2. School of Mechatronics Engn, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China

    Hongzhong Huang

  3. College of Information Engineering, Dalian Maritime University, Dalian, Liaoning, 116023, China

    Yaohua Hu

  4. School of Energy and Power Engineering, Central South University, Changsha, Hunan, 410083, China

    Lihua Xue & Zhangming Shi

Authors
  1. Lihua Xue
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  2. Hongzhong Huang
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  3. Yaohua Hu
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  4. Zhangming Shi
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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. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiao-Feng Liao

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

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Xue, L., Huang, H., Hu, Y., Shi, Z. (2005). Neural Networks Based Multiplex Forecasting System of the End-Point of Copper Blow Period. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_129

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  • DOI: https://doi.org/10.1007/11427469_129

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25914-5

  • Online ISBN: 978-3-540-32069-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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