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Optimization of type-2 fuzzy controllers using the bee colony algorithm / Leticia Amador, Oscar Castillo.

By: Amador, Leticia,, autor
Contributor(s): Castillo, Oscar,, autor
Material type: materialTypeLabelE-bookSeries: (SpringerBriefs in applied sciences and technology, Computational intelligence, 2191-530X).Publisher: Cham, Switzerland : Springer, 2017Description: 1 recurso en línea.ISBN: 3319542958; 9783319542959.Subject: Lógica difusaOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
Preface; Contents; 1 Introduction; References; 2 Theory and Background; 2.1 Fuzzy Inference System; 2.1.1 Type-1 Fuzzy Logic Systems; 2.1.2 Interval Type-2 Fuzzy Logic Systems; 2.2 Fuzzy Controllers; References; 3 Problem Statements; 3.1 Water Tank Controller; 3.1.1 Model Equations of the Water Tank; 3.1.2 Design of the Fuzzy Controller; 3.2 Autonomous Mobile Robot Controller; 3.2.1 General Description of Problem; 3.2.2 Fuzzy Logic Control Design; References; 4 Bee Colony Optimization Algorithm; 4.1 Proposed Method; References; 5 Simulation Results for the Proposed Methods.
5.1 Simulation Results for the Water Tank Controller5.1.1 Results for the Original Bee Colony Optimization Algorithm; 5.1.2 Results for the Fuzzy Bee Colony Optimization Algorithm for the Water Tank Controller; 5.2 Results for the Original BCO for an Autonomous Mobile Robot; 5.2.1 Results for the Fuzzy BCO for an Autonomous Mobile Robot; 6 Statistical Analysis and Comparison of Results; 7 Conclusions; Appendix; Bibliograpy; Index.
Abstract: 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.
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería QA9.64 A433 2017 EB (Browse shelf(Opens below)) Acceso electrónico eBook.20023511
Total holds: 0
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QA9.64 A383 2018 EB Advances in Fuzzy Logic and Technology 2017 Proceedings of: EUSFLAT- 2017 - The 10th Conference of the European Society for Fuzzy Logic and Technology, September 11-15, 2017, Warsaw, Poland IWIFSGN'2017 - The Sixteenth International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, September 13-15, 2017, Warsaw, Poland, Volume 3 QA9.64 A383 2018 EB Advances in Fuzzy Logic and Technology 2017 Proceedings of: EUSFLAT-2017 - The 10th Conference of the European Society for Fuzzy Logic and Technology, September 11-15, 2017, Warsaw, Poland IWIFSGN'2017 - The Sixteenth International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, September 13-15, 2017, Warsaw, Poland, Volume 1 QA9.64 A383 2018 EB Advances in Fuzzy Logic and Technology 2017 Proceedings of: EUSFLAT- 2017 - The 10th Conference of the European Society for Fuzzy Logic and Technology, September 11-15, 2017, Warsaw, Poland IWIFSGN'2017 - The Sixteenth International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, September 13-15, 2017, Warsaw, Poland, Volume 2 QA9.64 A433 2017 EB Optimization of type-2 fuzzy controllers using the bee colony algorithm QA9.64 A748 2016 EB A Practical Introduction to Fuzzy Logic using LISP QA9.64 A836 2017 EB Intuitionistic fuzzy logics QA9.64 B377 2016 EB A first course in fuzzy logic, fuzzy dynamical systems, and biomathematics : theory and applications

SpringerLink Springer Engineering eBooks 2017 English+International

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Preface; Contents; 1 Introduction; References; 2 Theory and Background; 2.1 Fuzzy Inference System; 2.1.1 Type-1 Fuzzy Logic Systems; 2.1.2 Interval Type-2 Fuzzy Logic Systems; 2.2 Fuzzy Controllers; References; 3 Problem Statements; 3.1 Water Tank Controller; 3.1.1 Model Equations of the Water Tank; 3.1.2 Design of the Fuzzy Controller; 3.2 Autonomous Mobile Robot Controller; 3.2.1 General Description of Problem; 3.2.2 Fuzzy Logic Control Design; References; 4 Bee Colony Optimization Algorithm; 4.1 Proposed Method; References; 5 Simulation Results for the Proposed Methods.

5.1 Simulation Results for the Water Tank Controller5.1.1 Results for the Original Bee Colony Optimization Algorithm; 5.1.2 Results for the Fuzzy Bee Colony Optimization Algorithm for the Water Tank Controller; 5.2 Results for the Original BCO for an Autonomous Mobile Robot; 5.2.1 Results for the Fuzzy BCO for an Autonomous Mobile Robot; 6 Statistical Analysis and Comparison of Results; 7 Conclusions; Appendix; Bibliograpy; Index.

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.

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