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LEADER 00000cam a2200769Ii 4500
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020 0128026871
020 9780128026878
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082 4 006.3|b23
082 4 006.3|b23
099 eBook O'Reilly for Public Libraries
100 1 Kayacan, Erdal,|eauthor.
245 10 Fuzzy neural networks for real time control applications :
|bconcepts, modeling and algorithms for fast learning /
|cErdal Kayacan & Mojtaba Ahmadieh Khanewsar with foreword
by Jerry M. Mendel.|h[O'Reilly electronic resource]
264 1 Amsterdam :|bButterworth-Heinemann is an imprint of
Elsevier,|c[2015].
264 4 |c©2016
300 1 online resource.
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
504 Includes bibliographical references and index.
505 0 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
505 8 2.3.1.2 Rule Base2.3.1.3 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
505 8 4.1 Type-1 Takagi-Sugeno-Kang Model4.2 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
505 8 5.2.5 GD Algorithm with a Momentum Term5.3 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
520 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.
588 0 Online resource; title from PDF title page (EBSCO, viewed
October 15, 2015).
590 O'Reilly|bO'Reilly Online Learning: Academic/Public
Library Edition
650 0 Neural networks (Computer science)
650 0 Fuzzy systems.
650 6 Réseaux neuronaux (Informatique)
650 6 Systèmes flous.
650 7 Fuzzy systems|2fast
650 7 Neural networks (Computer science)|2fast
700 1 Khanesar, Mojtaba Ahmadieh,|eauthor.
700 1 Mendel, Jerry M.,|eauthor of foreword.
776 08 |iPrint version:|aKayacan, Erdal|tFuzzy Neural Networks
for Real Time Control Applications : Concepts, Modeling
and Algorithms for Fast Learning|d: Elsevier Science,c2015
|z9780128026878
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
learning.oreilly.com/library/view/~/9780128027035/?ar
|zAvailable on O'Reilly for Public Libraries
880 8 |6505-00/(S|a6.3 Square-Root Filtering6.4 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 σ; 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
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