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Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications

  • Book
  • © 2020

Overview

Editors:
  1. Oscar Castillo

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

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  2. Patricia Melin

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

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    You can also search for this editor in PubMed   Google Scholar

  3. Janusz Kacprzyk

    1. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

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  • Describes recent advances in fuzzy logic, neural networks, and optimization algorithms, as well as their hybrid combinations
  • Presents applications in areas such as intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction, and optimization of complex problems
  • Written by experts in the field

Part of the book series: Studies in Computational Intelligence (SCI, volume 862)

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Table of contents (53 chapters)

  1. Front Matter

    Pages i-xiv
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  2. Type-1 and Type-2 Fuzzy Logic

    1. Front Matter

      Pages 1-1
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    2. Parameter Adaptation in the Imperialist Competitive Algorithm Using Generalized Type-2 Fuzzy Logic

      • Emer Bernal, Oscar Castillo, José Soria, Fevrier Valdez
      Pages 3-10

    3. Applying Fuzzy Logic to Identify Heterogeneity of the Allometric Response in Arithmetical Space

      • Cecilia Leal-Ramírez, Héctor Echavarría-Heras, Enrique Villa-Diharce
      Pages 11-34

    4. Fireworks Algorithm (FWA) with Adaptation of Parameters Using Interval Type-2 Fuzzy Logic System

      • Juan Barraza, Fevrier Valdez, Patricia Melin, Claudia I. González
      Pages 35-47

    5. Omnidirectional Four Wheel Mobile Robot Control with a Type-2 Fuzzy Logic Behavior-Based Strategy

      • Felizardo Cuevas, Oscar Castillo, Prometeo Cortés-Antonio
      Pages 49-62

    6. Optimization for Type-1 and Interval Type-2 Fuzzy Systems for the Classification of Blood Pressure Load Using Genetic Algorithms

      • Juan Carlos Guzmán, Patricia Melin, German Prado-Arechiga
      Pages 63-71

  3. Intuitionistic Fuzzy Logic

    1. Front Matter

      Pages 73-73
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    2. Interval Valued Intuitionistic Fuzzy Evaluations for Analysis of Students’ Knowledge

      • Evdokia Sotirova, Anthony Shannon, Vassia Atanassova, Krassimir Atanassov, Veselina Bureva
      Pages 75-82

    3. Generalized Net Model of the Network for Automatic Turning and Setting the Lighting in the Room with Intuitionistic Fuzzy Estimations

      • Tihomir Videv, Sotir Sotirov, Boris Bozveliev
      Pages 83-90

    4. Generalized Net Model of Common Internet Payment Gateway with Intuitionistic Fuzzy Estimations

      • Boris Bozveliev, Sotir Sotirov, Stanislav Simeonov, Tihomir Videv
      Pages 91-98

    5. Intuitionistic Fuzzy Neural Networks with Interval Valued Intuitionistic Fuzzy Conditions

      • Krassimir Atanassov, Sotir Sotirov, Nora Angelova
      Pages 99-106

    6. Generalised Atanassov Intuitionistic Fuzzy Sets Are Actually Intuitionistic Fuzzy Sets

      • Peter Vassilev, Krassimir Atanassov
      Pages 107-117

    7. The Numerical Solution of Intuitionistic Fuzzy Differential Equations by the Third Order Runge-Kutta Nyström Method

      • Bouchra Ben Amma, Said Melliani, S. Chadli
      Pages 119-132

    8. Intuitionistic Fuzzy Linear Systems

      • Hafida Atti, Bouchra Ben Amma, Said Melliani, S. Chadli
      Pages 133-144

    9. Nonlocal Intuitionistic Fuzzy Differential Equation

      • R. Ettoussi, Said Melliani, S. Chadli
      Pages 145-153

  4. Metaheuristics: Theory and Applications

    1. Front Matter

      Pages 155-155
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    2. Harmony Search with Dynamic Adaptation of Parameters for the Optimization of a Benchmark Controller

      • Cinthia Peraza, Fevrier Valdez, Oscar Castillo
      Pages 157-168

    3. Evaluation of Parallel Exploration and Exploitation Capabilities in Two PSO Variants with Intra Communication

      • Yunkio Kawano, Fevrier Valdez, Oscar Castillo
      Pages 169-184

    4. Chemical Reaction Algorithm to Control Problems

      • David de la O, Oscar Castillo, José Soria
      Pages 185-193
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Keywords

About this book

This book describes the latest advances in fuzzy logic, neural networks, and optimization algorithms, as well as their hybrid intelligent combinations, and their applications in the areas such as intelligent control, robotics, pattern recognition, medical diagnosis, time series prediction, and optimization. The topic is highly relevant as most current intelligent systems and devices use some form of intelligent feature to enhance their performance. The book also presents new and advanced models and algorithms of type-2 fuzzy logic and intuitionistic fuzzy systems, which are of great interest to researchers in these areas. Further, it proposes novel, nature-inspired optimization algorithms and innovative neural models. Featuring contributions on theoretical aspects as well as applications, the book appeals to a wide audience.

Editors and Affiliations

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

    Oscar Castillo, Patricia Melin

  • Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

Bibliographic Information

  • Book Title: Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications

  • Editors: Oscar Castillo, Patricia Melin, Janusz Kacprzyk

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-030-35445-9

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: Springer Nature Switzerland AG 2020

  • Hardcover ISBN: 978-3-030-35444-2Published: 28 February 2020

  • Softcover ISBN: 978-3-030-35447-3Published: 28 February 2021

  • eBook ISBN: 978-3-030-35445-9Published: 27 February 2020

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XIV, 792

  • Number of Illustrations: 123 b/w illustrations, 240 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence

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