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Optimization of Type-2 Fuzzy Controllers Using the Bee Colony Algorithm [electronic resource] / by Leticia Amador, Oscar Castillo.

By: Amador, Leticia [author.].
Contributor(s): Castillo, Oscar [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Computational Intelligence: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2017Edition: 1st ed. 2017.Description: VIII, 71 p. 40 illus., 31 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319542959.Subject(s): Computational intelligence | Artificial intelligence | Mathematical optimization | Calculus of variations | Control engineering | Robotics | Automation | Algorithms | Computational Intelligence | Artificial Intelligence | Calculus of Variations and Optimization | Control, Robotics, Automation | AlgorithmsAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Introduction -- Theory and Background -- Problems Statement -- Bee Colony Optimization Algorithm.-Simulation results for the Proposed Methods -- Statistical Analysis and Comparison of Results -- Conclusions.
In: Springer Nature eBookSummary: 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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Introduction -- Theory and Background -- Problems Statement -- Bee Colony Optimization Algorithm.-Simulation results for the Proposed Methods -- Statistical Analysis and Comparison of Results -- Conclusions.

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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