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Nature-Inspired Optimization of Type-2 Fuzzy Systems

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Nature-Inspired Computation and Machine Learning (MICAI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8857))

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Abstract

A review of the optimization methods used in the design of type-2 fuzzy systems, which are relatively novel models of imprecision, is presented in this paper. The main aim of the work is to study the basic reasons for optimizing type-2 fuzzy systems for solving problems different areas of application. Recently, nature-inspired methods have emerged as powerful optimization algorithms for solving complex problems. In the case of designing type-2 fuzzy systems for particular applications, the use of nature-inspired optimization methods have helped in the complex task of finding the appropriate parameter values and structure of the fuzzy systems. In this paper, we consider the application of genetic algorithms, particle swarm optimization and ant colony optimization as three different paradigms that help in the design of optimal type-2 fuzzy systems. A comparison of the different optimization methods for the case of designing type-2 fuzzy systems is also offered.

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Author information

Authors and Affiliations

  1. Division of Graduate Studies Tijuana, Tijuana Institute of Technology, Mexico

    Oscar Castillo, Patricia Melin & Fevrier Valdez

Authors
  1. Oscar Castillo
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  2. Patricia Melin
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  3. Fevrier Valdez
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Editor information

Editors and Affiliations

  1. Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan Dios Bátiz s/n, Col. Nueva Industrial Vallejo, 07738, Mexico City, Mexico

    Alexander Gelbukh

  2. Universidad Autónoma del Estado de Hidalgo, Área Académica de Computación y Electrónica, Carretera Pachuca-Tulancingo, , Km. 4.5, Col. Carboneras, Mineral de la Reforma,, 42180, Hidalgo, Mexico

    Félix Castro Espinoza

  3. Facultad de ciencias, Universidad Autónoma Nacional de México, Ciudad Universitaria, México DF, Mexico

    Sofía N. Galicia-Haro

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Castillo, O., Melin, P., Valdez, F. (2014). Nature-Inspired Optimization of Type-2 Fuzzy Systems. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds) Nature-Inspired Computation and Machine Learning. MICAI 2014. Lecture Notes in Computer Science(), vol 8857. Springer, Cham. https://doi.org/10.1007/978-3-319-13650-9_30

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  • DOI: https://doi.org/10.1007/978-3-319-13650-9_30

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13649-3

  • Online ISBN: 978-3-319-13650-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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