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Extension of the Fuzzy Sugeno Integral Based on Generalized Type-2 Fuzzy Logic

  • Book
  • © 2020

Overview

Authors:
  1. Patricia Melin

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

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  2. Gabriela E. Martinez

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

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    You can also search for this author in PubMed   Google Scholar

  • Presents an extension of the aggregation operator of the generalized interval type-2 Sugeno integral using generalized type-2 fuzzy logic
  • Demonstrates implementation in a modular neural network applied to face recognition and in an edge detector
  • Offers a brief introduction to the potential use of the aggregation operators in real-world applications
  • Discusses the basic concepts of type-1, interval type-2 and generalized type-2 fuzzy logic

Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)

Part of the book sub series: SpringerBriefs in Computational Intelligence (BRIEFSINTELL)

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Table of contents (5 chapters)

  1. Front Matter

    Pages i-xiii
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  2. Introduction to the Type-2 Fuzzy Sugeno Integral

    • Patricia Melin, Gabriela E. Martinez
    Pages 1-3

  3. Basic Theory for the Type-2 Fuzzy Sugeno Integral

    • Patricia Melin, Gabriela E. Martinez
    Pages 5-27

  4. Proposed Method for the Type-2 Fuzzy Sugeno Integral

    • Patricia Melin, Gabriela E. Martinez
    Pages 29-36

  5. Simulation Results of the Type-2 Fuzzy Sugeno Integral

    • Patricia Melin, Gabriela E. Martinez
    Pages 37-51

  6. Conclusions and Future Work on the Generalized Type-2 Fuzzy Sugeno Integral

    • Patricia Melin, Gabriela E. Martinez
    Pages 53-55

  7. Back Matter

    Pages 57-65
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Keywords

About this book

This book presents an extension of the aggregation operator of the generalized interval type-2 Sugeno integral using generalized type-2 fuzzy logic. This extension enables it to handle higher levels of uncertainty when adding any number of sources and types of information in a wide variety of decision-making applications. The authors also demonstrate that the extended aggregation operator offers better performance than other traditional or extended operators. The book is a valuables reference resource for students and researchers working on theory and applications of fuzzy logic in various areas of application where decision making is performed under high levels of uncertainty, such as pattern recognition, time series prediction, intelligent control and manufacturing.

Reviews

“The exposure of the material is well structured and abundant detailed numeric examples are a visible asset of the publication. Overall, a useful reading material of interest to those involved in fuzzy aggregation models, their generalizations and applications.” (Witold Pedrycz, zbMath 1417.68003, 2019)

Authors and Affiliations

  • Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin, Gabriela E. Martinez

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