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Application of Evidence Theory and Neural Network in Warning System of Financial Risk

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3497))

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Abstract

In warning system of financial risk, there are different relations between financial risk and factors. So the traditional prediction method can’t get the accurate result. Based on the theory of economical forecast and warning, this paper gives a new method that uses neural network to estimate deterministic factors and experts to forecast uncertain factors, to get the ideal result by evidential combination of the predictions come from the certain and uncertain factors.

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Author information

Authors and Affiliations

  1. Automation Department of Chongqing University, Chongqing, 400044, China

    Qingyu Xiong, Yinlin Huang, Shan Liang, Weiren Shi, Songsong Tan & Yinhua Lin

  2. The Key Lab of High Voltage Engineering & Electrical New Technology of, Education Ministry of China, Chongqing, 400044, China

    Qingyu Xiong

Authors
  1. Qingyu Xiong
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  2. Yinlin Huang
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  3. Shan Liang
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  4. Weiren Shi
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  5. Songsong Tan
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  6. Yinhua Lin
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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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Xiong, Q., Huang, Y., Liang, S., Shi, W., Tan, S., Lin, Y. (2005). Application of Evidence Theory and Neural Network in Warning System of Financial Risk. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_146

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  • DOI: https://doi.org/10.1007/11427445_146

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25913-8

  • Online ISBN: 978-3-540-32067-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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