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Bio-Inspired Optimization of Type-2 Fuzzy Controllers

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Springer Handbook of Computational Intelligence

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Abstract

A review of the bio-inspired optimization methods used in the design of type-2 fuzzy systems, which are relatively novel models of imprecision, is considered in this chapter. The fundamental focus of the work is based on the basic reasons for the need for optimization of type-2 fuzzy systems for different areas of application. Recently, bio-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 bio-inspired optimization methods has helped in the complex task of finding the appropriate parameter values and structure of fuzzy systems. In this chapter, 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. We also provide a comparison of the different optimization methods for the case of designing type-2 fuzzy systems.

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Abbreviations

ACO:

ant colony optimization

cos:

center of set

FLS:

fuzzy logic system

FOU:

footprint of uncertainty

GA:

genetic algorithm

MISO:

multiple inputs-single output

PSO:

particle swarm optimization

S-ACO:

simple ant colony optimization

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Authors and Affiliations

  1. Tijuana Institute of Technology, Calzada Tecnolo-gico s/n, 22379, Tijuana, Mexico

    Oscar Castillo

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  1. Oscar Castillo
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Correspondence to Oscar Castillo .

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Editors and Affiliations

  1. Systems Research Inst., Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

  2. Dep. Electrical and Computer Engineering, University of Alberta, 116 Street 9107, T6J 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

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Castillo, O. (2015). Bio-Inspired Optimization of Type-2 Fuzzy Controllers. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_78

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  • DOI: https://doi.org/10.1007/978-3-662-43505-2_78

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43504-5

  • Online ISBN: 978-3-662-43505-2

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