Development and applications of a sequential, minimal, radial basis function (RBF) neural network learning algorithm

This thesis presents a new sequential learning algorithm for realizing a minimal Radial Basis Function (RBF) neural network, referred to as M-RAN (Minimal Resource Allocation Network). Unlike most of the classical RBF neural networks with the number of hidden neurons fixed apriori, the network st...

Full description

Saved in:

Bibliographic Details
Main Author:	Lu, Ying Wei.
Other Authors:	Narasimhan, Sundararajan
Format:	Theses and Dissertations
Published:	2010
Subjects:	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Online Access:	http://hdl.handle.net/10356/39014
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Nanyang Technological University

id	sg-ntu-dr.10356-39014
record_format	dspace
spelling	sg-ntu-dr.10356-390142023-07-04T15:27:29Z Development and applications of a sequential, minimal, radial basis function (RBF) neural network learning algorithm Lu, Ying Wei. Narasimhan, Sundararajan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems This thesis presents a new sequential learning algorithm for realizing a minimal Radial Basis Function (RBF) neural network, referred to as M-RAN (Minimal Resource Allocation Network). Unlike most of the classical RBF neural networks with the number of hidden neurons fixed apriori, the network structure is dynamic in the proposed M-RAN algorithm. Master of Engineering 2010-05-21T03:45:06Z 2010-05-21T03:45:06Z 1997 1997 Thesis http://hdl.handle.net/10356/39014 NANYANG TECHNOLOGICAL UNIVERSITY 123 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
topic	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Lu, Ying Wei. Development and applications of a sequential, minimal, radial basis function (RBF) neural network learning algorithm
description	This thesis presents a new sequential learning algorithm for realizing a minimal Radial Basis Function (RBF) neural network, referred to as M-RAN (Minimal Resource Allocation Network). Unlike most of the classical RBF neural networks with the number of hidden neurons fixed apriori, the network structure is dynamic in the proposed M-RAN algorithm.
author2	Narasimhan, Sundararajan
author_facet	Narasimhan, Sundararajan Lu, Ying Wei.
format	Theses and Dissertations
author	Lu, Ying Wei.
author_sort	Lu, Ying Wei.
title	Development and applications of a sequential, minimal, radial basis function (RBF) neural network learning algorithm
title_short	Development and applications of a sequential, minimal, radial basis function (RBF) neural network learning algorithm
title_full	Development and applications of a sequential, minimal, radial basis function (RBF) neural network learning algorithm
title_fullStr	Development and applications of a sequential, minimal, radial basis function (RBF) neural network learning algorithm
title_full_unstemmed	Development and applications of a sequential, minimal, radial basis function (RBF) neural network learning algorithm
title_sort	development and applications of a sequential, minimal, radial basis function (rbf) neural network learning algorithm
publishDate	2010
url	http://hdl.handle.net/10356/39014
_version_	1772826144809156608

Development and applications of a sequential, minimal, radial basis function (RBF) neural network learning algorithm

Similar Items