Radial Basis Function (RBF) Neural Network Control for Mechanical Systems Design, Analysis and Matlab Simulation /

Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design metho...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Liu, Jinkun (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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020 |a 9783642348167  |9 978-3-642-34816-7 
024 7 |a 10.1007/978-3-642-34816-7  |2 doi 
040 |d GrThAP 
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100 1 |a Liu, Jinkun.  |e author. 
245 1 0 |a Radial Basis Function (RBF) Neural Network Control for Mechanical Systems  |h [electronic resource] :  |b Design, Analysis and Matlab Simulation /  |c by Jinkun Liu. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg :  |b Imprint: Springer,  |c 2013. 
300 |a XV, 365 p.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
505 0 |a Introduction -- RBF Neural Network Design and Simulation -- RBF Neural Network Control Based on Gradient Descent Algorithm -- Adaptive RBF Neural Network Control -- Neural Network Sliding Mode Control -- Adaptive RBF Control Based on Global Approximation -- Adaptive Robust RBF Control Based on Local Approximation -- Backstepping Control with RBF -- Digital RBF Neural Network Control -- Discrete Neural Network Control -- Adaptive RBF Observer Design and Sliding Mode Control. 
520 |a Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design.   This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronautics. 
650 0 |a Engineering. 
650 0 |a Neural networks (Computer science). 
650 0 |a Computational intelligence. 
650 0 |a Vibration. 
650 0 |a Dynamical systems. 
650 0 |a Dynamics. 
650 0 |a Control engineering. 
650 1 4 |a Engineering. 
650 2 4 |a Control. 
650 2 4 |a Vibration, Dynamical Systems, Control. 
650 2 4 |a Computational Intelligence. 
650 2 4 |a Mathematical Models of Cognitive Processes and Neural Networks. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783642348150 
856 4 0 |u http://dx.doi.org/10.1007/978-3-642-34816-7  |z Full Text via HEAL-Link 
912 |a ZDB-2-ENG 
950 |a Engineering (Springer-11647)