Skip to main content
main-content

Über dieses Buch

This book reviews current state of the art methods for building intelligent systems using type-2 fuzzy logic and bio-inspired optimization techniques. Combining type-2 fuzzy logic with optimization algorithms, powerful hybrid intelligent systems have been built using the advantages that each technique offers. This book is intended to be a reference for scientists and engineers interested in applying type-2 fuzzy logic for solving problems in pattern recognition, intelligent control, intelligent manufacturing, robotics and automation. This book can also be used as a reference for graduate courses like the following: soft computing, intelligent pattern recognition, computer vision, applied artificial intelligence, and similar ones. We consider that this book can also be used to get novel ideas for new lines of re-search, or to continue the lines of research proposed by the authors.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract
A review of the optimization methods used in the design of type-2 fuzzy systems, which are relatively novel models of imprecision, is presented in this book. The fundamental focus of the book is based on the basic reasons of the need for optimizing type-2 fuzzy systems for different areas of application. Recently, bio-inspired methods have emerged as powerful optimization algorithms for solving complex problems. In the case of designing type-2 fuzzy systems for particular applications, the use of bio-inspired optimization methods have helped in the complex task of finding the appropriate parameter values and the right structure of the fuzzy systems. In this book, we review the application of genetic algorithms, particle swarm optimization and ant colony optimization, as three different paradigms that help in the design of optimal type-2 fuzzy systems. We also provide a comparison of results for the different optimization methods for the case of designing type-2 fuzzy systems.
Oscar Castillo, Patricia Melin

Chapter 2. Type-2 Fuzzy Logic Systems

Abstract
In this chapter, a brief overview of the basic concepts of type-2 fuzzy systems is presented. This overview is intended to provide the basic concepts needed to understand the methods and algorithms presented later in this book.
Oscar Castillo, Patricia Melin

Chapter 3. Bio-Inspired Optimization Methods

Abstract
In this chapter a brief overview of the basic concepts from bio-inspired optimization methods needed for this work is presented. In particular, the methods that are covered in this chapter are: particle swarm optimization, genetic algorithms and ant colony optimization.
Oscar Castillo, Patricia Melin

Chapter 4. Overview of Genetic Algorithms Applied in the Optimization of Type-2 Fuzzy Systems

Abstract
There have been many works reported in the literature optimizing type-2 fuzzy systems using different kinds of genetic algorithms. Most of these works have had relative success according to the different areas of application. In this chapter, we offer a representative review of these types of works to illustrate the advantages of using a bio-inspired optimization technique for automating the design process of type-2 fuzzy systems. This overview has the goal of providing the reader with an idea of the diversity of applications that have been achieved using genetic algorithms for type-2 fuzzy system optimization.
Oscar Castillo, Patricia Melin

Chapter 5. Particle Swarm Optimization in the Design of Type-2 Fuzzy Systems

Abstract
There have been several works reported in the literature optimizing type-2 fuzzy systems using different kinds of PSO algorithms. Most of these works have had relative success according to the different areas of application. In this chapter, we offer a representative review of these types of works to illustrate the advantages of using the PSO optimization technique for automating the design process of type-2 fuzzy systems.
Oscar Castillo, Patricia Melin

Chapter 6. Ant Colony Optimization Algorithms for the Design of Type-2 Fuzzy Systems

Abstract
There have also been several works reported in the literature optimizing type-2 fuzzy systems using different kinds of Ant Colony Optimization algorithms. Most of these works have had relative success according to the different areas of application. In this chapter, we offer a representative and brief review of these types of works to illustrate the advantages of using the ACO optimization techniques for automating the design process or parameters of type-2 fuzzy systems.
Oscar Castillo, Patricia Melin

Chapter 7. Other Methods for Optimization of Type-2 Fuzzy Systems

Abstract
In this chapter we describe some other works reported in the literature optimizing type-2 fuzzy systems using different kinds of optimization algorithms (other than GAs, PSO or ACO, which were covered in previous chapters). Most of these works have had relative success according to the different areas of application. In this chapter, we offer a representative and brief review of these types of works to illustrate the advantages of using the corresponding optimization techniques for automating the design process or parameters of type-2 fuzzy systems.
Oscar Castillo, Patricia Melin

Chapter 8. Simulation Results Illustrating the Optimization of Type-2 Fuzzy Controllers

Abstract
In this chapter we describe as an illustration the optimization of the membership functions’ parameters of a type-2 fuzzy logic controller in order to find the optimal intelligent controller for an autonomous wheeled mobile robot.
Oscar Castillo, Patricia Melin

Chapter 9. Genetic Optimization of Interval Type-2 Fuzzy Systems for Hardware Implementation on FPGAs

Abstract
This chapter proposes a method for the design of a Type-2 Fuzzy Logic Controller (FLC-T2) and a Type-1 Fuzzy Logic Controller (FLC-T1) using Genetic Algorithms. The two controllers were tested with different levels of uncertainty to Regulate Speed in a Direct Current Motor (ReSDCM). The controllers were synthesized in Very High Description Language (VHDL) code for a Field Programmable Gate Array (FPGA), using the Xilinx System Generator (XSG) of Xilinx ISE and Matlab-Simulink. Comparisons were made between the FLC-T1 versus FLC-T2 in VHDL code and also with a Proportional Integral Differential (PID) Controller, to ReSDCM. To evaluate the difference in performance of the three types of controllers, the t-student statistical test was used.
Oscar Castillo, Patricia Melin

Chapter 10. General Overview of the Area and Future Trends

Abstract
In this chapter a general overview of the area of type-2 fuzzy system optimization is presented. Also, possible future trends that we can envision based on the review of this area are presented. It has been well-known for a long time, that designing fuzzy systems is a difficult task, and this is especially true in the case of type-2 fuzzy systems. The use of GAs, ACO and PSO in designing type-1 fuzzy systems has become a standard practice for automatically designing this sort of systems.
Oscar Castillo, Patricia Melin

Backmatter

Weitere Informationen

Premium Partner

    Bildnachweise