
Fuzzy Neural Networks for Real Time Control Applications
Concepts, Modeling and Algorithms for Fast Learning
Description
Key Features
- Parameter update algorithms for type-1 and type-2 fuzzy neural networks and their stability analysis
- Contains algorithms that are applicable to real time systems
- Introduces fast and simple adaptation rules for type-1 and type-2 fuzzy neural networks
- Number of case studies both in identification and control
- Provides MATLAB® codes for some algorithms in the book
Readership
Graduate students and engineers in the control engineering area; in particular real time control systems.
Table of Contents
-
- Dedication
- Foreword
- Preface
- Acknowledgments
- List of Acronyms/Abbreviations
- Chapter 1: Mathematical Preliminaries
- Abstract
- 1.1 Introduction
- 1.2 Linear Matrix Algebra
- 1.3 Function
- 1.4 Stability Analysis
- 1.5 Sliding Mode Control Theory
- 1.6 Conclusion
- Chapter 2: Fundamentals of Type-1 Fuzzy Logic Theory
- Abstract
- 2.1 Introduction
- 2.2 Type-1 Fuzzy Sets
- 2.3 Basics of Fuzzy Logic Control
- 2.4 Pros and Cons of Fuzzy Logic Control
- 2.5 Western and Eastern Perspectives on Fuzzy Logic
- 2.6 Conclusion
- Chapter 3: Fundamentals of Type-2 Fuzzy Logic Theory
- Abstract
- 3.1 Introduction
- 3.2 Type-2 Fuzzy Sets
- 3.3 Existing Type-2 Membership Functions
- 3.4 Conclusion
- Chapter 4: Type-2 Fuzzy Neural Networks
- Abstract
- 4.1 Type-1 Takagi-Sugeno-Kang Model
- 4.2 Other Takagi-Sugeno-Kang Models
- 4.3 Conclusion
- Chapter 5: Gradient Descent Methods for Type-2 Fuzzy Neural Networks
- Abstract
- 5.1 Introduction
- 5.2 Overview of Iterative Gradient Descent Methods
- 5.3 Gradient Descent Based Learning Algorithms for Type-2 Fuzzy Neural Networks
- 5.4 Stability Analysis
- 5.5 Further Reading
- 5.6 Conclusion
- Chapter 6: Extended Kalman Filter Algorithm for the Tuning of Type-2 Fuzzy Neural Networks
- Abstract
- 6.1 Introduction
- 6.2 Discrete Time Kalman Filter
- 6.3 Square-Root Filtering
- 6.4 Extended Kalman Filter Algorithm
- 6.5 Extended Kalman Filter Training of Type-2 Fuzzy Neural Networks
- 6.6 Decoupled Extended Kalman Filter
- 6.7 Conclusion
- Chapter 7: Sliding Mode Control Theory-Based Parameter Adaptation Rules for Fuzzy Neural Networks
- Abstract
- 7.1 Introduction
- 7.2 Identification Design
- 7.3 Controller Design
- 7.4 Conclusion
- Chapter 8: Hybrid Training Method for Type-2 Fuzzy Neural Networks Using Particle Swarm Optimization
- Abstract
- 8.1 Introduction
- 8.2 Continuous Version of Particle Swarm Optimization
- 8.3 Analysis of Continuous Version of Particle Swarm Optimization
- 8.4 Proposed Hybrid Training Algorithm for Type-2 Fuzzy Neural Network
- 8.5 Conclusion
- Chapter 9: Noise Reduction Property of Type-2 Fuzzy Neural Networks
- Abstract
- 9.1 Introduction
- 9.2 Type-2 Fuzzy Neural System Structure
- 9.3 Conclusion
- Chapter 10: Case Studies: Identification Examples
- Abstract
- 10.1 Identification of Mackey-Glass Time Series
- 10.2 Identification of Second-Order Nonlinear Time-Varying Plant
- 10.3 Analysis and Discussion
- 10.4 Conclusion
- Chapter 11: Case Studies: Control Examples
- Abstract
- 11.1 Control of Bispectral Index of a Patient During Anesthesia
- 11.2 Control of Magnetic Rigid Spacecraft
- 11.3 Control of Autonomous Tractor
- 11.4 Conclusion
- Appendix A
- Appendix B
- Index
Product details
- No. of pages: 264
- Language: English
- Copyright: © Butterworth-Heinemann 2015
- Published: September 17, 2015
- Imprint: Butterworth-Heinemann
- Paperback ISBN: 9780128026878
- eBook ISBN: 9780128027035
About the Authors
Erdal Kayacan

His research areas are flight mechanics and control, unmanned aerial vehicles, robotics, mechatronics, soft computing methods, iterative learning control techniques, sliding mode control and model predictive control.
Dr. Kayacan is a Senior Member of IEEE. He is currently serving as an editor for Journal on Automation and Control Engineering (JACE) and editorial advisory board in Grey Systems Theory and Application.
Has published over 20 papers in international peer-reviewed Journals, and presented 25 international Conference papers.
Affiliations and Expertise
Mojtaba Khanesar
In 2010, he has held a 9 months visiting student position at Bogazici University, Istanbul, Turkey. He is currently an assistant professor in the Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran. His current research interests are identification, sliding mode control, adaptive controller design, fuzzy systems and intelligent optimization.
Dr. Khanesar is a member of IEEE technical committee on soft computing and IEEE technical committee on networked control systems. He is currently serving as a member of the editorial board for International Journal of Social Computing and Cyber-Physical Systems. He has published over 12 papers in international peer-reviewed Journals, and presented over 20 international Conference papers.
Affiliations and Expertise
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