000			03266nam a22005177a 4500
001			sulb-eb0024404
003			BD-SySUS
005			20160413122438.0
007			cr nn 008mamaa
008			130125s2013 gw | s |||| 0|eng d
020			_a9783642348167 _9978-3-642-34816-7
024	7		_a10.1007/978-3-642-34816-7 _2doi
050		4	_aTJ212-225
072		7	_aTJFM _2bicssc
072		7	_aTEC004000 _2bisacsh
082	0	4	_a629.8 _223
100	1		_aLiu, Jinkun. _eauthor.
245	1	0	_aRadial Basis Function (RBF) Neural Network Control for Mechanical Systems _h[electronic resource] : _bDesign, Analysis and Matlab Simulation / _cby Jinkun Liu.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2013.
300			_aXV, 365 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_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.
520			_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.
650		0	_aEngineering.
650		0	_aNeural networks (Computer science).
650		0	_aComputational intelligence.
650		0	_aVibration.
650		0	_aDynamical systems.
650		0	_aDynamics.
650		0	_aControl engineering.
650	1	4	_aEngineering.
650	2	4	_aControl.
650	2	4	_aVibration, Dynamical Systems, Control.
650	2	4	_aComputational Intelligence.
650	2	4	_aMathematical Models of Cognitive Processes and Neural Networks.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642348150
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-34816-7
912			_aZDB-2-ENG
942			_2Dewey Decimal Classification _ceBooks
999			_c46496 _d46496