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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.
Automatic control. --- Chemometrics. --- Neural networks (Computer science). --- Radial basis functions. --- Mechanical Engineering --- Engineering & Applied Sciences --- Mechanical Engineering - General --- Neural networks (Computer science) --- Basis functions, Radial --- Functions, Radial basis --- Radial basis function method --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Control engineering --- Control equipment --- Engineering. --- Computational intelligence. --- Vibration. --- Dynamical systems. --- Dynamics. --- Control engineering. --- Control. --- Vibration, Dynamical Systems, Control. --- Computational Intelligence. --- Mathematical Models of Cognitive Processes and Neural Networks. --- Control theory --- Engineering instruments --- Automation --- Programmable controllers --- Dynamical systems --- Kinetics --- Mathematics --- Mechanics, Analytic --- Force and energy --- Mechanics --- Physics --- Statics --- Cycles --- Sound --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Natural computation --- Construction --- Industrial arts --- Technology --- Approximation theory --- Control and Systems Theory. --- Neural networks (Computer science) .
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