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A Novel Idea of Real-Time Fuzzy Switching Regression Analysis: A Nuclear Power Plants Case Study

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Integrated Uncertainty Management and Applications

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 68))

Abstract

In this paper, the concept of regression models is extended to handle hybrid data from various sources that quite often exhibit diverse levels of data quality specifically in nuclear power plants. The major objective of this study is to develop a convex hull method as a potential vehicle which reduces the computing time, especially in the case of real-time data analysis as well as minimizes the computational complexity. We propose an efficient real-time fuzzy switching regression analysis based on a convex hull approach, in which a beneath-beyond algorithm is used in building a convex hull when alleviating limitations of a linear programming in system modeling. Additionally, the method addresses situations when we have to deal with heterogeneous data.

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Author information

Authors and Affiliations

  1. Graduate School of Information, Production and Systems (IPS), Waseda University, 2-7, Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka-ken, 808-0135, Japan

    Azizul Azhar Ramli & Junzo Watada

Authors
  1. Azizul Azhar Ramli
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  2. Junzo Watada
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Editor information

Editors and Affiliations

  1. School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, 923-1292, Ishikawa, Japan

    Van-Nam Huynh  & Yoshiteru Nakamori  & 

  2. Department of Engineering Mathematics, University of Bristol, Queen’s Building, University Walk, BS8 1TR, Bristol, United Kingdom

    Jonathan Lawry

  3. Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, 560-8531, Osaka, Japan

    Masahiro Inuiguchi

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Ramli, A.A., Watada, J. (2010). A Novel Idea of Real-Time Fuzzy Switching Regression Analysis: A Nuclear Power Plants Case Study. In: Huynh, VN., Nakamori, Y., Lawry, J., Inuiguchi, M. (eds) Integrated Uncertainty Management and Applications. Advances in Intelligent and Soft Computing, vol 68. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11960-6_49

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  • DOI: https://doi.org/10.1007/978-3-642-11960-6_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11959-0

  • Online ISBN: 978-3-642-11960-6

  • eBook Packages: EngineeringEngineering (R0)

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