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Economic Turning Point Forecasting Using Neural Network with Weighted Fuzzy Membership Functions

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New Trends in Applied Artificial Intelligence (IEA/AIE 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4570))

Abstract

This paper proposes a new forecasting model based on neural network with weighted fuzzy membership functions (NEWFM) concerning forecasting of turning points in business cycle by the composite index. NEWFM is a new model of neural networks to improve forecasting accuracy by using self adaptive weighted fuzzy membership functions. The locations and weights of the membership functions are adaptively trained, and then the fuzzy membership functions are combined by bounded sum. The implementation of the NEWFM demonstrates an excellent capability in the field of business cycle analysis.

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Author information

Authors and Affiliations

  1. College of Software, Kyungwon University, Sungnam 461-701, Korea

    Soo H. Chai & Joon S. Lim

Authors
  1. Soo H. Chai
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  2. Joon S. Lim
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Editor information

Hiroshi G. Okuno Moonis Ali

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© 2007 Springer Berlin Heidelberg

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Chai, S.H., Lim, J.S. (2007). Economic Turning Point Forecasting Using Neural Network with Weighted Fuzzy Membership Functions. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_15

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  • DOI: https://doi.org/10.1007/978-3-540-73325-6_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73322-5

  • Online ISBN: 978-3-540-73325-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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