TY - BOOK ID - 8061451 TI - Radial basis function (RBF) neural network control for mechanical systems : design, analysis and Matlab simulation PY - 2013 SN - 364243455X 3642348157 3642348165 1299337597 PB - Berlin ; New York : Beijing : Springer ; Tsinghua Univ. Press, DB - UniCat KW - Automatic control. KW - Chemometrics. KW - Neural networks (Computer science). KW - Radial basis functions. KW - Mechanical Engineering KW - Engineering & Applied Sciences KW - Mechanical Engineering - General KW - Neural networks (Computer science) KW - Basis functions, Radial KW - Functions, Radial basis KW - Radial basis function method KW - Artificial neural networks KW - Nets, Neural (Computer science) KW - Networks, Neural (Computer science) KW - Neural nets (Computer science) KW - Control engineering KW - Control equipment KW - Engineering. KW - Computational intelligence. KW - Vibration. KW - Dynamical systems. KW - Dynamics. KW - Control engineering. KW - Control. KW - Vibration, Dynamical Systems, Control. KW - Computational Intelligence. KW - Mathematical Models of Cognitive Processes and Neural Networks. KW - Control theory KW - Engineering instruments KW - Automation KW - Programmable controllers KW - Dynamical systems KW - Kinetics KW - Mathematics KW - Mechanics, Analytic KW - Force and energy KW - Mechanics KW - Physics KW - Statics KW - Cycles KW - Sound KW - Intelligence, Computational KW - Artificial intelligence KW - Soft computing KW - Natural computation KW - Construction KW - Industrial arts KW - Technology KW - Approximation theory KW - Control and Systems Theory. KW - Neural networks (Computer science) . UR - https://www.unicat.be/uniCat?func=search&query=sysid:8061451 AB - 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. ER -