book

Fuzzy Neural Networks for Real Time Control Applications

by Erdal Kayacan, Mojtaba Ahmadieh Khanesar

October 2015

Intermediate to advanced

264 pages

6h 2m

English

Butterworth-Heinemann

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Abstract1.1 Introduction1.2 Linear Matrix Algebra1.3 Function1.4 Stability Analysis1.5 Sliding Mode Control Theory1.6 Conclusion

Abstract2.1 Introduction2.2 Type-1 Fuzzy Sets2.3 Basics of Fuzzy Logic Control2.4 Pros and Cons of Fuzzy Logic Control2.5 Western and Eastern Perspectives on Fuzzy Logic2.6 Conclusion
Abstract3.1 Introduction3.2 Type-2 Fuzzy Sets3.3 Existing Type-2 Membership Functions3.4 Conclusion
Abstract4.1 Type-1 Takagi-Sugeno-Kang Model4.2 Other Takagi-Sugeno-Kang Models4.3 Conclusion
Abstract5.1 Introduction5.2 Overview of Iterative Gradient Descent Methods5.3 Gradient Descent Based Learning Algorithms for Type-2 Fuzzy Neural Networks5.4 Stability Analysis5.5 Further Reading5.6 Conclusion
Abstract6.1 Introduction6.2 Discrete Time Kalman Filter6.3 Square-Root Filtering6.4 Extended Kalman Filter Algorithm6.5 Extended Kalman Filter Training of Type-2 Fuzzy Neural Networks6.6 Decoupled Extended Kalman Filter6.7 Conclusion
Abstract7.1 Introduction7.2 Identification Design
Abstract8.1 Introduction8.2 Continuous Version of Particle Swarm Optimization8.3 Analysis of Continuous Version of Particle Swarm Optimization8.4 Proposed Hybrid Training Algorithm for Type-2 Fuzzy Neural Network
Abstract9.1 Introduction9.2 Type-2 Fuzzy Neural System Structure9.3 Conclusion
Abstract10.1 Identification of Mackey-Glass Time Series10.2 Identification of Second-Order Nonlinear Time-Varying Plant10.3 Analysis and Discussion10.4 Conclusion
Abstract11.1 Control of Bispectral Index of a Patient During Anesthesia11.2 Control of Magnetic Rigid Spacecraft11.3 Control of Autonomous Tractor11.4 Conclusion

Overview

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