Radial Basis Function (RBF) Neural Network Control for Mechanical Systems: Design, Analysis and Matlab Simulation [1 ed.] 978-3-642-34815-0, 978-3-642-34816-7
Radial Basis Function (RBF) Neural Network Controlfor Mechanical Systems is motivated by the need for systematic design
302 92 41KB
English Pages 365 [373] Year 2013
Table of contents :
Front Matter....Pages i-xv
Introduction....Pages 1-17
RBF Neural Network Design and Simulation....Pages 19-53
RBF Neural Network Control Based on Gradient Descent Algorithm....Pages 55-69
Adaptive RBF Neural Network Control....Pages 71-112
Neural Network Sliding Mode Control....Pages 113-132
Adaptive RBF Control Based on Global Approximation....Pages 133-191
Adaptive Robust RBF Control Based on Local Approximation....Pages 193-249
Backstepping Control with RBF....Pages 251-292
Digital RBF Neural Network Control....Pages 293-309
Discrete Neural Network Control....Pages 311-337
Adaptive RBF Observer Design and Sliding Mode Control....Pages 339-362
Back Matter....Pages 363-365
Front Matter....Pages i-xv
Introduction....Pages 1-17
RBF Neural Network Design and Simulation....Pages 19-53
RBF Neural Network Control Based on Gradient Descent Algorithm....Pages 55-69
Adaptive RBF Neural Network Control....Pages 71-112
Neural Network Sliding Mode Control....Pages 113-132
Adaptive RBF Control Based on Global Approximation....Pages 133-191
Adaptive Robust RBF Control Based on Local Approximation....Pages 193-249
Backstepping Control with RBF....Pages 251-292
Digital RBF Neural Network Control....Pages 293-309
Discrete Neural Network Control....Pages 311-337
Adaptive RBF Observer Design and Sliding Mode Control....Pages 339-362
Back Matter....Pages 363-365
![Radial Basis Function (RBF) Neural Network Control for Mechanical Systems: Design, Analysis and Matlab Simulation [1 ed.]
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- Jinkun Liu (auth.)
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- Jointly published with Tsinghua University Press, Beijing2013
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