000 | 03236nam a22005175i 4500 | ||
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001 | 978-3-642-34816-7 | ||
003 | DE-He213 | ||
005 | 20200421111155.0 | ||
007 | cr nn 008mamaa | ||
008 | 130125s2013 gw | s |||| 0|eng d | ||
020 |
_a9783642348167 _9978-3-642-34816-7 |
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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. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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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 | _cEBK | ||
999 |
_c53477 _d53477 |