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Optimization of Type-2 Fuzzy Controllers Using the Bee Colony Algorithm

  • Book
  • © 2017

Overview

Authors:
  1. Leticia Amador

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

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  2. Oscar Castillo

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

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    You can also search for this author in PubMed   Google Scholar

  • Focuses on the fields of fuzzy logic, bio-inspired algorithm; especially bee colony optimization algorithm
  • Serves as reference for scientists and engineers interested in applying fuzzy logic techniques for solving problems in intelligent control
  • Presents recent research
  • Includes supplementary material: sn.pub/extras

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 (7 chapters)

  1. Front Matter

    Pages i-viii
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  2. Introduction

    • Leticia Amador, Oscar Castillo
    Pages 1-6

  3. Theory and Background

    • Leticia Amador, Oscar Castillo
    Pages 7-11

  4. Problem Statements

    • Leticia Amador, Oscar Castillo
    Pages 13-21

  5. Bee Colony Optimization Algorithm

    • Leticia Amador, Oscar Castillo
    Pages 23-32

  6. Simulation Results for the Proposed Methods

    • Leticia Amador, Oscar Castillo
    Pages 33-54

  7. Statistical Analysis and Comparison of Results

    • Leticia Amador, Oscar Castillo
    Pages 55-56

  8. Conclusions

    • Leticia Amador, Oscar Castillo
    Pages 57-58

  9. Back Matter

    Pages 59-71
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Keywords

About this book

This book focuses on the fields of fuzzy logic, bio-inspired algorithm; especially bee colony optimization algorithm and also considering the fuzzy control area. The main idea is that this areas together can to solve various control problems and to find better results. In this book we test the proposed method using two benchmark problems; the problem for filling a water tank and the problem for controlling the trajectory in an autonomous mobile robot. When Interval Type-2 Fuzzy Logic System is implemented to model the behavior of systems, the results show a better stabilization, because the analysis of uncertainty is better. For this reason we consider in this book the proposed method using fuzzy systems, fuzzy controllers, and bee colony optimization algorithm improve the behavior of the complex control problems.

Authors and Affiliations

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

    Leticia Amador, Oscar Castillo

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