Transient improvement via neural and switching control

Transient improvement via neural and switching control

This thesis contains three main results. The first result deals with an in-depth discussion of the radial basis function network where a training algorithm is proposed and the convergence of the RBF network is analyzed. The proof of convergence is based on the finite cover theory and the geometric g...

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Bibliographic Details
Main Author: Xu, Fang
Other Authors: Soh, Yeng Chai
Format: Theses and Dissertations
Language:English
Published: 2009
Subjects:
DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
Online Access:http://hdl.handle.net/10356/19571
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Institution: Nanyang Technological University
Language: English
Description
Summary:This thesis contains three main results. The first result deals with an in-depth discussion of the radial basis function network where a training algorithm is proposed and the convergence of the RBF network is analyzed. The proof of convergence is based on the finite cover theory and the geometric growth criterion, which is the basis of the RBF network training algorithm.

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