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Fuzzy Neural Networks for Real Time Control Applications
Concepts, Modeling and Algorithms for Fast Learning
This book presents the basics of fuzzy neural networks, in particular type-2 fuzzy neural networks, for the identification and learning control of real time systems. In addition to conventional parameter tuning methods, e.g. GD, SMC theory-based learning algorithms, which are simple and have closed forms, and their stability analysis have also been introduced. This book has been 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.
Erdal Kayacan (Author), Mojtaba Ahmadieh Khanesar (Author)
9780128026878
Paperback, published 17 September 2015
264 pages
22.9 x 15.1 x 1.8 cm, 0.43 kg
Approx.242 pages
Dedication Preface Acknowledgements List of Acronyms/Abbreviations/Index terms 1- Mathematical Preliminaries 2- Fundamentals of Type-1 Fuzzy Logic Theory 3- Fundamentals of Type-2 Fuzzy Logic Theory 4- Type-2 Fuzzy Neural Networks 5- Gradient Descent Methods for Type-2 Fuzzy Neural Networks 6- Extended Kalman Filter Algorithm for the tuning of Type-2 Fuzzy Neural Networks 7- Sliding Mode Control Theory-Based Parameter Adaptation Rules for Fuzzy Neural Networks 8- Hybrid Training Method for Type-2 Fuzzy Neural Networks Using Particle Swarm Optimization 9- Noise Reduction Property of Type-2 Fuzzy Neural Networks 10- Case Studies: Identification Examples 11- Case Studies: Control Examples Appendix
Subject Areas: Artificial intelligence [UYQ], Mathematical logic [PBCD]
