LEADER 04003nam  22007695  450 
001 9910437917903321
005 20251113210613.0
010   $a9781299337596
010   $a1299337597
010   $a9783642348167
010   $a3642348165
024 7 $a10.1007/978-3-642-34816-7
035   $a(OCoLC)826122634
035   $a(MiFhGG)GVRL6UPY
035   $a(CKB)2670000000337162
035   $a(MiAaPQ)EBC1082844
035   $a(MiFhGG)9783642348167
035   $a(DE-He213)978-3-642-34816-7
035   $a(EXLCZ)992670000000337162
100   $a20130125d2013      u|         0
101 0 $aeng
135   $aurun|---uuuua
181   $ctxt
182   $cc
183   $acr
200 10$aRadial Basis Function (RBF) Neural Network Control for Mechanical Systems $eDesign, Analysis and Matlab Simulation /$fby Jinkun Liu
205   $a1st ed. 2013.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2013.
215   $a1 online resource (xv, 365 pages) $cillustrations
225 0 $aGale eBooks
300   $a"With 170 figures".
311 08$a9783642434556 
311 08$a364243455X 
311 08$a9783642348150 
311 08$a3642348157 
320   $aIncludes bibliographical references and index.
327   $aIntroduction -- 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.
330   $aRadial 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.
606   $aControl engineering
606   $aMultibody systems
606   $aVibration
606   $aMechanics, Applied
606   $aComputational intelligence
606   $aNeural networks (Computer science)
606   $aControl and Systems Theory
606   $aMultibody Systems and Mechanical Vibrations
606   $aComputational Intelligence
606   $aMathematical Models of Cognitive Processes and Neural Networks
615  0$aControl engineering.
615  0$aMultibody systems.
615  0$aVibration.
615  0$aMechanics, Applied.
615  0$aComputational intelligence.
615  0$aNeural networks (Computer science).
615 14$aControl and Systems Theory.
615 24$aMultibody Systems and Mechanical Vibrations.
615 24$aComputational Intelligence.
615 24$aMathematical Models of Cognitive Processes and Neural Networks.
676   $a629.895
700   $aLiu$b Jinkun$f1965-$01761240
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910437917903321
996   $aRadial basis function (RBF) neural network control for mechanical systems$94200569
997   $aUNINA