Fuzzy Neural Networks for Real Time Control Applications

Concepts, Modeling and Algorithms for Fast Learning
 
 
Butterworth-Heinemann (Verlag)
  • 1. Auflage
  • |
  • erschienen am 7. Oktober 2015
  • |
  • 264 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-0-12-802703-5 (ISBN)
 

AN INDISPENSABLE RESOURCE FOR ALL THOSE WHO DESIGN AND IMPLEMENT TYPE-1 AND TYPE-2 FUZZY NEURAL NETWORKS IN REAL TIME SYSTEMS

Delve into the type-2 fuzzy logic systems and become engrossed in the parameter update algorithms for type-1 and type-2 fuzzy neural networks and their stability analysis with this book!

Not only does this book stand apart from others in its focus but also in its application-based presentation style. Prepared in a way that can be easily understood by those who are experienced and inexperienced in this field. Readers can benefit from the computer source codes for both identification and control purposes which are given at the end of the book.

A clear and an in-depth examination has been made of all the necessary mathematical foundations, type-1 and type-2 fuzzy neural network structures and their learning algorithms as well as their stability analysis.

You will find that each chapter is devoted to a different learning algorithm for the tuning of type-1 and type-2 fuzzy neural networks; some of which are:

• Gradient descent

• Levenberg-Marquardt

• Extended Kalman filter

In addition to the aforementioned conventional learning methods above, number of novel sliding mode control theory-based learning algorithms, which are simpler and have closed forms, and their stability analysis have been proposed. Furthermore, hybrid methods consisting of particle swarm optimization and sliding mode control theory-based algorithms have also been introduced.

The potential readers of this book are expected to be the undergraduate and graduate students, engineers, mathematicians and computer scientists. Not only can this book be used as a reference source for a scientist who is interested in fuzzy neural networks and their real-time implementations but also as a course book of fuzzy neural networks or artificial intelligence in master or doctorate university studies. We hope that this book will serve its main purpose successfully.


  • 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


Erdal Kayacan received a B.Sc. degree in electrical engineering from Istanbul Technical University, Istanbul, Turkey, in 2003 and a M.Sc. degree in systems and control engineering from Bogazici University, Istanbul, Turkey, in 2006. In September 2011, he received a Ph.D. degree in electrical and electronic engineering at Bogazici University, Istanbul, Turkey. After finishing his post-doctoral research in KU Leuven at the division of mechatronics, biostatistics and sensors (MeBioS), he is currently pursuing his research in Nanyang Technological University at the School of Mechanical and Aerospace Engineering as an assistant professor.
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.
  • Englisch
  • USA
Elsevier Science
  • 7,87 MB
978-0-12-802703-5 (9780128027035)
0128027037 (0128027037)
weitere Ausgaben werden ermittelt
  • Front Cover
  • Fuzzy Neural Networks Forreal Time Control Applications: Concepts, Modeling and Algorithms for Fast Learning
  • Copyright
  • Dedication
  • Contents
  • Foreword
  • References
  • Preface
  • Acknowledgments
  • List of Acronyms/Abbreviations
  • Chapter 1: Mathematical Preliminaries
  • 1.1 Introduction
  • 1.2 Linear Matrix Algebra
  • 1.3 Function
  • 1.4 Stability Analysis
  • 1.5 Sliding Mode Control Theory
  • 1.6 Conclusion
  • References
  • Chapter 2: Fundamentals of Type-1 Fuzzy Logic Theory
  • 2.1 Introduction
  • 2.2 Type-1 Fuzzy Sets
  • 2.3 Basics of Fuzzy Logic Control
  • 2.3.1 FLC Block Diagram
  • 2.3.1.1 Fuzzification
  • 2.3.1.2 Rule Base
  • 2.3.1.3 Inference
  • 2.3.1.4 Defuzzification
  • 2.4 Pros and Cons of Fuzzy Logic Control
  • 2.5 Western and Eastern Perspectives on Fuzzy Logic
  • 2.6 Conclusion
  • References
  • Chapter 3: Fundamentals of Type-2 Fuzzy Logic Theory
  • 3.1 Introduction
  • 3.2 Type-2 Fuzzy Sets
  • 3.2.1 Interval Type-2 Fuzzy Sets
  • 3.2.2 T2FLS Block Diagram
  • 3.2.2.1 Fuzzifier
  • 3.2.2.2 Rule Base
  • 3.2.2.3 Inference
  • 3.2.2.4 Type Reduction
  • 3.2.2.5 Defuzzification
  • 3.3 Existing Type-2 Membership Functions
  • 3.3.1 A Novel Type-2 MF: Elliptic MF
  • 3.4 Conclusion
  • References
  • Chapter 4: Type-2 Fuzzy Neural Networks
  • 4.1 Type-1 Takagi-Sugeno-Kang Model
  • 4.2 Other Takagi-Sugeno-Kang Models
  • 4.2.1 Model I
  • 4.2.2 Model II
  • 4.2.2.1 Interval Type-2 TSK FLS
  • 4.2.2.2 Numerical Example of the Interval Type-2 TSK FLS
  • 4.2.3 Model III
  • 4.3 Conclusion
  • References
  • Chapter 5: Gradient Descent Methods for Type-2 Fuzzy Neural Networks
  • 5.1 Introduction
  • 5.2 Overview of Iterative Gradient Descent Methods
  • 5.2.1 Basic Gradient-Descent Optimization Algorithm
  • 5.2.2 Newton and Gauss-Newton Optimization Algorithms
  • 5.2.3 LM Algorithm
  • 5.2.4 Gradient Descent Algorithm with an Adaptive Learning Rate
  • 5.2.5 GD Algorithm with a Momentum Term
  • 5.3 Gradient Descent Based Learning Algorithms for Type-2 Fuzzy Neural Networks
  • 5.3.1 Consequent Part Parameters
  • 5.3.2 Premise Part Parameters
  • 5.3.3 Variants of the Back-Propagation Algorithm for Training the T2FNNs
  • 5.4 Stability Analysis
  • 5.4.1 Stability Analysis of GD for Training of T2FNN
  • 5.4.2 Stability Analysis of the LM for Training of T2FNN
  • 5.5 Further Reading
  • 5.6 Conclusion
  • References
  • Chapter 6: Extended Kalman Filter Algorithm for the Tuning of Type-2 Fuzzy Neural Networks
  • 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
  • References
  • Chapter 7: Sliding Mode Control Theory-Based Parameter Adaptation Rules for Fuzzy Neural Networks
  • 7.1 Introduction
  • 7.2 Identification Design
  • 7.2.1 Identification Using Gaussian Type-2 MF with Uncertain s
  • 7.2.1.1 Parameter Update Rules for the T2FNN
  • 7.2.1.2 Proof of Theorem 7.1
  • 7.2.2 Identification Using T2FNN with Elliptic Type-2 MF
  • 7.2.2.1 Parameter Update Rules for the T2FNN
  • 7.2.2.2 Proof of Theorem 7.2
  • 7.3 Controller Design
  • 7.3.1 Control Scheme Incorporating a T2FNN Structure
  • 7.3.1.1 T2FNN
  • 7.3.2 SMC Theory-Based Learning Algorithm for T2FNN with Gaussian MFs with Uncertain s Values
  • 7.3.2.1 Parameter Update Rules For T2FNN
  • 7.3.2.2 Proof of Theorem 7.3
  • 7.3.3 SMC Theory-Based Learning Algorithm for T2FNN with Elliptic MFs
  • 7.3.3.1 Proof of Theorem 7.4
  • 7.3.3.2 Proof of Theorem 7.5
  • 7.4 Conclusion
  • References
  • Chapter 8: Hybrid Training Method for Type-2 Fuzzy Neural Networks Using Particle Swarm Optimization
  • 8.1 Introduction
  • 8.1.1 Fully PSO Training Algorithms
  • 8.1.2 Hybrid PSO with Computation-based Training Methods
  • 8.2 Continuous Version of Particle Swarm Optimization
  • 8.3 Analysis of Continuous Version of Particle Swarm Optimization
  • 8.3.1 Stability Analysis
  • 8.3.2 Dynamic Behavior
  • 8.3.3 Higher-Order Continuous PSO
  • 8.4 Proposed Hybrid Training Algorithm for Type-2 Fuzzy Neural Network
  • 8.4.1 Proposed Hybrid Training Algorithm for T2FNN with Type-2 MFs with Uncertain s
  • 8.4.1.1 T2FNN with Type-2 MFs with Uncertain s
  • 8.4.1.2 Proposed Hybrid Training Algorithm for T2FNN by Using Type-2 MFs with Uncertain s
  • 8.4.1.3 Stability Analysis
  • 8.4.2 Proposed Hybrid Training Algorithm for T2FNN With Type-2 MFs with Uncertain c
  • 8.4.2.1 T2FNN with Type-2 MFs with Uncertain c
  • 8.4.2.2 Proposed Hybrid Training Algorithm for T2FNN with MFs with Uncertain Center Value
  • 8.4.2.3 Stability Analysis
  • 8.4.3 Further Discussion About the Hybrid Training Method
  • 8.5 Conclusion
  • References
  • Chapter 9: Noise Reduction Property of Type-2 Fuzzy Neural Networks
  • 9.1 Introduction
  • 9.2 Type-2 Fuzzy Neural System Structure
  • 9.2.1 Elliptic MF
  • 9.2.2 Structure of the T2FLS
  • 9.2.3 Noise Reduction Property of the Proposed Type-2 MF
  • 9.2.3.1 Case I
  • 9.2.3.2 Case II
  • 9.3 Conclusion
  • References
  • Chapter 10: Case Studies: Identification Examples
  • 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
  • References
  • Chapter 11: Case Studies: Control Examples
  • 11.1 Control of Bispectral Index of a Patient During Anesthesia
  • 11.1.1 Realistic Patient Model
  • 11.1.2 Simulation Results
  • 11.2 Control of Magnetic Rigid Spacecraft
  • 11.2.1 Dynamic Model of a Magnetic Satellite
  • 11.2.2 Simulation Results
  • 11.3 Control of Autonomous Tractor
  • 11.3.1 Mathematical Description of Tractor
  • 11.3.1.1 Kinematic Model
  • 11.3.1.2 Yaw Dynamics Model
  • 11.3.2 Overall Control Scheme
  • 11.3.2.1 Kinematic Controller
  • 11.3.2.2 Dynamic Controllers
  • 11.3.3 Experimental Results
  • 11.4 Conclusion
  • References
  • Appendix A
  • A.1 SMC Theory-Based Training Algorithm for Type-2 Fuzzy Neural Network
  • A.1.1 Main Script for SMC Theory-Based Training Algorithm for T2FNN
  • A.1.2 Source Code for Matlab Function t2fnnsmc
  • A.1.3 Source Code for Matlab Function MatrixMultiple.m
  • A.1.4 Source Code for Matlab Function scale.m
  • A.1.5 Source Code for Matlab Function inv_scale.m
  • A.2 GD-Based Theory-Based Training Algorithm for Type-2 Fuzzy Neural Network
  • A.2.1 Main Script
  • A.2.2 Source Code for Matlab Function t2fnngd.m
  • A.3 KF-Based Training Algorithm for Type-2 Fuzzy Neural Network
  • A.3.1 Main Script
  • A.3.2 Source Code for Matlab Function t2fnnkalman.m
  • Appendix B
  • B.1 SMC Theory-Based Training Algorithm for Type-2 Fuzzy Neural Network Controller
  • B.1.1 Main Script
  • B.1.2 Source Code for MATLAB Function neurofuzzy
  • B.1.3 Source Code for MATLAB Function elip.m
  • Index
  • Back Cover

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