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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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

id	sg-ntu-dr.10356-19571
record_format	dspace
spelling	sg-ntu-dr.10356-195712023-07-04T15:28:33Z Transient improvement via neural and switching control Xu, Fang Soh, Yeng Chai School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering 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. Master of Engineering 2009-12-14T06:15:52Z 2009-12-14T06:15:52Z 1998 1998 Thesis http://hdl.handle.net/10356/19571 en NANYANG TECHNOLOGICAL UNIVERSITY 111 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Xu, Fang Transient improvement via neural and switching control
description	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.
author2	Soh, Yeng Chai
author_facet	Soh, Yeng Chai Xu, Fang
format	Theses and Dissertations
author	Xu, Fang
author_sort	Xu, Fang
title	Transient improvement via neural and switching control
title_short	Transient improvement via neural and switching control
title_full	Transient improvement via neural and switching control
title_fullStr	Transient improvement via neural and switching control
title_full_unstemmed	Transient improvement via neural and switching control
title_sort	transient improvement via neural and switching control
publishDate	2009
url	http://hdl.handle.net/10356/19571
_version_	1772825573967527936

Transient improvement via neural and switching control

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