LEADER 03141nam 2200529Ia 450 001 9910437917903321 005 20200520144314.0 010 $a1-299-33759-7 010 $a3-642-34816-5 024 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(EXLCZ)992670000000337162 100 $a20130131d2013 uy 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 $edesign, analysis and Matlab simulation /$fJinkun Liu 205 $a1st ed. 2013. 210 $aBerlin ;$aNew York $cSpringer ;$aBeijing $cTsinghua Univ. Press$dc2013 215 $a1 online resource (xv, 365 pages) $cillustrations 225 0 $aGale eBooks 300 $a"With 170 figures". 311 $a3-642-43455-X 311 $a3-642-34815-7 320 $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 $aAutomatic control 606 $aNeural networks (Computer science) 606 $aRadial basis functions 615 0$aAutomatic control. 615 0$aNeural networks (Computer science) 615 0$aRadial basis functions. 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