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Application of Rough Fuzzy Neural Network in Iron Ore Import Risk Early-Warning

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Advances in Neural Networks - ISNN 2010 (ISNN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6064))

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Abstract

This paper identifies factors for iron ore import risk early warning. Application of rough set theory for Chinese iron ore import risk factors and test data reduction has been introduced to construct rough fuzzy neural network model of iron ore import risk assessment. By employing monthly data of 2004.1-2008.12 for model training, we use this model to forecast 10 groups (2009.1-2009.10) of iron ore import risk early-warning indicators under conditions and to predict the actual test results and error analysis of data.

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Author information

Authors and Affiliations

  1. College of Resources and Safety Engineering, China University of Mining and Technology (Beijing), 100012, Beijing, China

    YunBing Hou & Juan Yang

Authors
  1. YunBing Hou
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  2. Juan Yang
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, 200240, Shanghai, China

    Liqing Zhang  & Bao-Liang Lu  & 

  2. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water bay, Kowloon, Hong Kong, China

    James Kwok

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Hou, Y., Yang, J. (2010). Application of Rough Fuzzy Neural Network in Iron Ore Import Risk Early-Warning. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_54

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  • DOI: https://doi.org/10.1007/978-3-642-13318-3_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13317-6

  • Online ISBN: 978-3-642-13318-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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