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Identification of a nonlinear industrial process via fuzzy clustering

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Methodology and Tools in Knowledge-Based Systems (IEA/AIE 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1415))

Abstract

This paper presents a fuzzy logic approach to complex system modeling that is based on fuzzy clustering technique. As compared with other modeling methods,the proposed approach has the advantage of simplicity, flexibility, and high accuracy. Further, it is easy to use and may be handled by an automatic procedure. An industrial process example (i.e, Heat exchanger) is provided to illustrate the performance of the proposed apprach.

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References

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Author information

Authors and Affiliations

  1. Dept of Electriacl and computer Engineering, University of Tehran, Tehran, Iran

    B. Moshiri & S. Chaychi. Maleki

  2. Intelligent Systems Reasearch Faculty, IPM, Tehran, Iran

    B. Moshiri

Authors
  1. B. Moshiri
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  2. S. Chaychi. Maleki
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Editor information

José Mira Angel Pasqual del Pobil Moonis Ali

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© 1998 Springer-Verlag

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Moshiri, B., Chaychi. Maleki, S. (1998). Identification of a nonlinear industrial process via fuzzy clustering. In: Mira, J., del Pobil, A.P., Ali, M. (eds) Methodology and Tools in Knowledge-Based Systems. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64582-9_809

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  • DOI: https://doi.org/10.1007/3-540-64582-9_809

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64582-5

  • Online ISBN: 978-3-540-69348-2

  • eBook Packages: Springer Book Archive

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