Fuzzy Neural Networks for Real Time Control Applications

Erdal Kayacan received a B.Sc. degree in electrical engineering from Istanbul Technical University, Istanbul, Turkey, in 2003 and a M.Sc. degree in systems and control engineering from Bogazici University, Istanbul, Turkey, in 2006. In September 2011, he received a Ph.D. degree in electrical and electronic engineering at Bogazici University, Istanbul, Turkey. After finishing his post-doctoral research in KU Leuven at the division of mechatronics, biostatistics and sensors (MeBioS), he is currently pursuing his research in Nanyang Technological University at the School of Mechanical and Aerospace Engineering as an assistant professor.His research areas are flight mechanics and control, unmanned aerial vehicles, robotics, mechatronics, soft computing methods, iterative learning control techniques, sliding mode control and model predictive control.Dr. Kayacan is a Senior Member of IEEE. He is currently serving as an editor for Journal on Automation and Control Engineering (JACE) and editorial advisory board in Grey Systems Theory and Application.Has published over 20 papers in international peer-reviewed Journals, and presented 25 international Conference papers. Mojtaba Ahmadieh Khanesar received a B.Sc. degree in Control Engineering from K. N. Toosi University of Technology, Tehran, Iran, in 2005 and a M.Sc. degree in control engineering from K. N. Toosi University of Technology, Tehran, Iran, in 2007. In February 2012, he received a Ph.D. degree in Control Engineering at K. N. Toosi University of Technology, Tehran, Iran.In 2010, he has held a 9 months visiting student position at Bogazici University, Istanbul, Turkey. He is currently an assistant professor in the Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran. His current research interests are identification, sliding mode control, adaptive controller design, fuzzy systems and intelligent optimization.Dr. Khanesar is a member of IEEE technical committee on soft computing and IEEE technical committee on networked control systems. He is currently serving as a member of the editorial board for International Journal of Social Computing and Cyber-Physical Systems. He has published over 12 papers in international peer-reviewed Journals, and presented over 20 international Conference papers.

• 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