Details
Sequential Intelligent Dynamic System Modeling and Control
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149,79 € |
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| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 05.07.2024 |
| ISBN/EAN: | 9789819715411 |
| Sprache: | englisch |
| Anzahl Seiten: | 260 |
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Beschreibungen
The book offers novel research results of sequential intelligent dynamic system modeling and control in a unified framework from theory proposals to real applications. It covers an in-depth study of various learning algorithms for the permanent adaptation of intelligent model parameters as well as of structural parts of the model. The comprehensive researches on sequential fuzzy and neural controller design schemes for some complex real applications are included. This is particularly suited for readers who are interested to learn practical solutions for controlling nonlinear systems that are uncertain and varied at any time. In addition, the organization of the book from addressing fundamental concepts, and presenting novel intelligent models to solving real applications is one of the major features of the book, which makes it a valuable resource for both beginners and researchers wanting to further their understanding and study about realtime online intelligent modeling and control ofnonlinear dynamic systems. The book can benefit researchers, engineers, and graduate students in the fields of control engineering, artificial intelligence, computational intelligence, intelligent control, nonlinear system modeling, and control, etc.<p></p>
<div>1 Fuzzy Inference Systems.- 2 Neural Networks.- 3 Optimization Algorithms.- 4 Modeling and Control of Nonlinear Dynamic Systems.- 5 Online Sequential Fuzzy Extreme Learning Machine.- 6 Sequential Adaptive Fuzzy Inference System.- 7 Evolving Fuzzy Systems based on Data Clouds.- 8 Stability of A Class of Evolving Fuzzy Systems.- 9 Adaptive Self-Learning Fuzzy Autopilot Design for Uncertain Bank-to-Turn Missiles.- 10 Self-Evolving Fuzzy Model-based Controller for Hypersonic Vehicle.- 11 Self-Evolving Data Cloud-based PID-like Controller for Nonlinear Uncertain Systems.- 12 Adaptive Nonparametric Evolving Fuzzy Controller for Nonlinear Uncertain Systems with Dead Zone.- 13 Adaptive Backstepping Neural Controller for Magnetic Bearing System.- 14 Simplified Adaptive Backstepping Neural Controller for Magnetic Bearing System.- 15 Robust Kernel-based Model Reference Adaptive Control for Unstable Aircraft.</div><p></p><p></p>
<div><b>Hai-Jun Rong</b> received the B.Eng. degree in precision instrument from Xi’an Technological University, Xi’an, China, in 2000, the M.Eng. degree in control theory and control engineering from Xi’an Jiaotong University, Xi’an, China, in 2003, and the Ph.D. degree in intelligent control from Nanyang Technological University, Singapore, in 2008. From December 2006 to October 2008, she was a Research Associate and a Research Fellow in Nanyang Technological University. She was a visiting scholar of Lancaster University in UK from 2016 to 2017 and also a distinguishing visiting scholar of University of Macau in 2018. Currently, she is working as a Professor in the School of Aerospace, Xi’an Jiaotong University. She is an Associate Editor of the Evolving Systems journal (Springer). She has been a member of the Program Committee of some IEEE international conferences. Her research interests include neural networks, fuzzy systems and intelligent control. She has published over 60 publications in leading journals and peerreviewed conference proceedings.<br></div><div><p> </p>
<p><b>Zhao-Xu Yang</b> received the B.Eng. degree in mechanical engineering and automation, the M.Eng. degree in spaceflight engineering, and the Ph.D. degree in aeronautical and astronautical science and technology from Xi‘an Jiaotong University, Xi’an, China, in 2011, 2013, and 2018, respectively. He is currently an Associate Professor with the School of Aerospace Engineering, Xi’an Jiaotong University, from 2018. He visited Lancaster University as a Visiting Researcher from July 2019 to November 2019. His research interests include neural networks, fuzzy systems, fault diagnosis, and intelligent control. He has published over 30 publications in leading journals and peerreviewed conference proceedings.</p></div>
<p><b>Zhao-Xu Yang</b> received the B.Eng. degree in mechanical engineering and automation, the M.Eng. degree in spaceflight engineering, and the Ph.D. degree in aeronautical and astronautical science and technology from Xi‘an Jiaotong University, Xi’an, China, in 2011, 2013, and 2018, respectively. He is currently an Associate Professor with the School of Aerospace Engineering, Xi’an Jiaotong University, from 2018. He visited Lancaster University as a Visiting Researcher from July 2019 to November 2019. His research interests include neural networks, fuzzy systems, fault diagnosis, and intelligent control. He has published over 30 publications in leading journals and peerreviewed conference proceedings.</p></div>
The book offers novel research results of sequential intelligent dynamic system modeling and control in a unified framework from theory proposals to real applications. It covers an in-depth study of various learning algorithms for the permanent adaptation of intelligent model parameters as well as of structural parts of the model. The comprehensive researches on sequential fuzzy and neural controller design schemes for some complex real applications are included. This is particularly suited for readers who are interested to learn practical solutions for controlling nonlinear systems that are uncertain and varied at any time. In addition, the organization of the book from addressing fundamental concepts, and presenting novel intelligent models to solving real applications is one of the major features of the book, which makes it a valuable resource for both beginners and researchers wanting to further their understanding and study about realtime online intelligent modeling and control of nonlinear dynamic systems. The book can benefit researchers, engineers, and graduate students in the fields of control engineering, artificial intelligence, computational intelligence, intelligent control, nonlinear system modeling, and control, etc.<p></p>
Comprehensively studies sequential fuzzy system modelling in dynamic environments Deepens sequential fuzzy and neural controller design both in theory and practice Presents interdisciplinary research between computational intelligence and control engineering
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