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 SYSTEMSDelve into the type-2 fuzzy logic systems and become engrossed in the parameter update algorithms for type-1 and type-2 fuzzy...
Leider schon ausverkauft
131.99 €
versandkostenfrei
Produktdetails
Produktinformationen zu „Fuzzy Neural Networks for Real Time Control Applications “
Weitere Produktinformationen zu „Fuzzy Neural Networks for Real Time Control Applications “
AN INDISPENSABLE RESOURCE FOR ALL THOSE WHO DESIGN AND IMPLEMENT TYPE-1 AND TYPE-2 FUZZY NEURAL NETWORKS IN REAL TIME SYSTEMSDelve 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 filterIn 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.
Bibliographische Angaben
- Autoren: Erdal Kayacan , Mojtaba Ahmadieh Khanesar
- Verlag: Elsevier Science & Technology
- EAN: 9780128026878
Inhaltsverzeichnis zu „Fuzzy Neural Networks for Real Time Control Applications “
DedicationPrefaceAcknowledgementsList of Acronyms/Abbreviations/Index terms1- Mathematical Preliminaries2- Fundamentals of Type-1 Fuzzy Logic Theory3- Fundamentals of Type-2 Fuzzy Logic Theory4- Type-2 Fuzzy Neural Networks5- Gradient Descent Methods for Type-2 Fuzzy Neural Networks6- Extended Kalman Filter Algorithm for the tuning of Type-2 Fuzzy Neural Networks7- Sliding Mode Control Theory-Based Parameter Adaptation Rules for Fuzzy Neural Networks8- Hybrid Training Method for Type-2 Fuzzy Neural Networks Using Particle Swarm Optimization9- Noise Reduction Property of Type-2 Fuzzy Neural Networks10- Case Studies: Identification Examples11- Case Studies: Control ExamplesAppendix
Kommentar zu "Fuzzy Neural Networks for Real Time Control Applications"
0 Gebrauchte Artikel zu „Fuzzy Neural Networks for Real Time Control Applications“
Zustand | Preis | Porto | Zahlung | Verkäufer | Rating |
---|
Schreiben Sie einen Kommentar zu "Fuzzy Neural Networks for Real Time Control Applications".
Kommentar verfassen