Radial basis function (RBF) neural network control for mechanical systems design, analysis and Matlab simulation /
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...
| Main Author: | Liu, Jinkun, 1965- |
|---|---|
| Other Authors: | SpringerLink (Online service) |
| Format: | eBook |
| Language: | English |
| Published: |
Berlin ; New York : Beijing :
Springer ; Tsinghua Univ. Press,
©2013.
Berlin ; New York : Beijing : [2013] |
| Physical Description: |
1 online resource. |
| Subjects: |
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.