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 eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
• Subjects: Engineering.; Neural networks (Computer science).; Computational intelligence.; Vibration.; Dynamical systems.; Dynamics.; Control engineering.; Control.; Vibration, Dynamical Systems, Control.; Computational Intelligence.; Mathematical Models of Cognitive Processes and Neural Networks.

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.