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Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering
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Radial Basis Function (RBF) Neural Network Control for Mechanical Systems - Design, Analysis and Matlab Simulation (Paperback, 2013 ed.)
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Radial Basis Function (RBF) Neural Network Control for Mechanical Systems - Design, Analysis and Matlab Simulation (Paperback, 2013 ed.)
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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.
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