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Nature-Inspired Optimization of Type-2 Fuzzy Logic Controllers

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Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making (INFUS 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1029))

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Abstract

In this paper we perform a comparison of the use of type-2 fuzzy logic in two nature-inspired methods: Ant Colony Optimization (ACO) and Gravitational Search Algorithm (GSA).

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References

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Author information

Authors and Affiliations

  1. Tijuana Institute of Technology, Calzada Tecnologico s/n, 22379, Tomas Aquino, Tijuana, Mexico

    Oscar Castillo

Authors
  1. Oscar Castillo
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Corresponding author

Correspondence to Oscar Castillo .

Editor information

Editors and Affiliations

  1. Management Faculty, Istanbul Technical University, Istanbul, Turkey

    Cengiz Kahraman

  2. Industrial Engineering Department, Yildiz Technical University, Istanbul, Turkey

    Selcuk Cebi

  3. Management Faculty, Istanbul Technical University, Istanbul, Turkey

    Sezi Cevik Onar

  4. Management Faculty, Istanbul Technical University, Ä°stanbul, Turkey

    Basar Oztaysi

  5. Industrial Engineering Department, Galatasaray University, Istanbul, Turkey

    A. Cagri Tolga

  6. Management Faculty, Istanbul Technical University, Istanbul, Turkey

    Irem Ucal Sari

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Castillo, O. (2020). Nature-Inspired Optimization of Type-2 Fuzzy Logic Controllers. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A., Sari, I. (eds) Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. INFUS 2019. Advances in Intelligent Systems and Computing, vol 1029. Springer, Cham. https://doi.org/10.1007/978-3-030-23756-1_2

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