Radial Basis Function (RBF) Neural Network Control for Mechanical Systems Design, Analysis and Matlab Simulation /

• Main Author: Liu, Jinkun. (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
• Subjects: Engineering.; Vibration.; Control.; Vibration, Dynamical Systems, Control.; Computational Intelligence.; Mathematical Models of Cognitive Processes and Neural Networks.
• Online Access: https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-642-34816-7

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 metho...

Table of Contents:

• Introduction
• 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.