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Fuzzy neural networks for real time control applications : concepts, modeling and algorithms for fast learning /
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 wit...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam :
Butterworth-Heinemann is an imprint of Elsevier,
[2015].
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Kayacan, Erdal, |e author. | |
| 245 | 1 | 0 | |a Fuzzy neural networks for real time control applications : |b concepts, modeling and algorithms for fast learning / |c Erdal Kayacan & Mojtaba Ahmadieh Khanewsar with foreword by Jerry M. Mendel. |
| 264 | 1 | |a Amsterdam : |b Butterworth-Heinemann is an imprint of Elsevier, |c [2015]. | |
| 264 | 4 | |c �2016 | |
| 300 | |a 1 online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed October 15, 2015). | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 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 | |a 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 | |a 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 | |a 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 | |a 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. | ||
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 0 | |a Fuzzy systems. | |
| 650 | 6 | |a R�eseaux neuronaux (Informatique) |0 (CaQQLa)201-0209597 | |
| 650 | 6 | |a Syst�emes flous. |0 (CaQQLa)201-0147670 | |
| 650 | 7 | |a COMPUTERS / General |2 bisacsh | |
| 650 | 7 | |a Fuzzy systems |2 fast |0 (OCoLC)fst00936814 | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast |0 (OCoLC)fst01036260 | |
| 700 | 1 | |a Khanesar, Mojtaba Ahmadieh, |e author. | |
| 700 | 1 | |a Mendel, Jerry M., |e author of foreword. | |
| 776 | 0 | 8 | |i Print version: |a Kayacan, Erdal |t Fuzzy Neural Networks for Real Time Control Applications : Concepts, Modeling and Algorithms for Fast Learning |d : Elsevier Science,c2015 |z 9780128026878 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780128026878 |z Texto completo |
| 880 | 8 | |6 505-00/(S |a 6.3 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 | |