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Robust TSK Fuzzy Modeling Approach Using Noise Clustering Concept for Function Approximation

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Computational and Information Science (CIS 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3314))

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Abstract

This paper proposes the algorithm that additional term is added to an objective function of noise clustering algorithm to define fuzzy subspaces in a fuzzy regression manner to identify fuzzy subspaces and parameters of the consequent parts simultaneously and obtain robust performance against outliers.

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Author information

Authors and Affiliations

  1. Department of electrical and electronic engineering, Yonsei University, 134, Shinchon-dong, Sudaemoon-gu, Seoul, Korea

    Kyoungjung Kim, Kyu Min Kyung, Euntai Kim & Mignon Park

  2. Korea electronics technology institute, 401-402 B/D 192, Yakdae-Dong, Wonmi-Gu, Buchon-Si, Kyunggi-Do, Korea

    Chang-Woo Park

Authors
  1. Kyoungjung Kim
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  2. Kyu Min Kyung
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  3. Chang-Woo Park
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  4. Euntai Kim
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  5. Mignon Park
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Editor information

Editors and Affiliations

  1. School of Electronic Science and Technology, Anhui University, Hefei, Anhui, China

    Jun Zhang

  2. College of Science, Donghua University, 1882 Yan’an Xilu Road, 20051, Shanghai, China

    Ji-Huan He

  3. BASICS, Department of Computer Science and Engineering, Shanghai Jiao Tong University, 200030, Shanghai, China

    Yuxi Fu

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© 2004 Springer-Verlag Berlin Heidelberg

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Kim, K., Kyung, K.M., Park, CW., Kim, E., Park, M. (2004). Robust TSK Fuzzy Modeling Approach Using Noise Clustering Concept for Function Approximation. In: Zhang, J., He, JH., Fu, Y. (eds) Computational and Information Science. CIS 2004. Lecture Notes in Computer Science, vol 3314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30497-5_84

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  • DOI: https://doi.org/10.1007/978-3-540-30497-5_84

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24127-0

  • Online ISBN: 978-3-540-30497-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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