The University of Southampton
University of Southampton Institutional Repository

Grey-box radial basis function modelling

Grey-box radial basis function modelling
Grey-box radial basis function modelling
A fundamental principle in data modelling is to incorporate available a priori information regarding the underlying data generating mechanism into the modelling process. We adopt this principle and consider grey-box radial basis function (RBF) modelling capable of incorporating prior knowledge. Specifically, we show how to explicitly incorporate the two types of prior knowledge: (i) the underlying data generating mechanism exhibits known symmetric property, and (ii) the underlying process obeys a set of given boundary value constraints. The class of efficient orthogonal least squares regression algorithms can readily be applied without any modification to construct parsimonious grey-box RBF models with enhanced generalisation capability.
0925-2312
1564-1571
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Harris, Chris
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Harris, Chris
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Chen, Sheng, Hong, Xia and Harris, Chris (2011) Grey-box radial basis function modelling Neurocomputing, 74, (10), pp. 1564-1571.

Record type: Article

Abstract

A fundamental principle in data modelling is to incorporate available a priori information regarding the underlying data generating mechanism into the modelling process. We adopt this principle and consider grey-box radial basis function (RBF) modelling capable of incorporating prior knowledge. Specifically, we show how to explicitly incorporate the two types of prior knowledge: (i) the underlying data generating mechanism exhibits known symmetric property, and (ii) the underlying process obeys a set of given boundary value constraints. The class of efficient orthogonal least squares regression algorithms can readily be applied without any modification to construct parsimonious grey-box RBF models with enhanced generalisation capability.

PDF neucom-2011-v74-n10-May.pdf - Version of Record
Restricted to Repository staff only
Request a copy

More information

Published date: May 2011
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 272264
URI: https://eprints.soton.ac.uk/id/eprint/272264
ISSN: 0925-2312
PURE UUID: 6e6e86f1-f9f1-4699-9171-5740716b8b06

Catalogue record

Date deposited: 09 May 2011 13:32
Last modified: 18 Jul 2017 06:32

Export record

Contributors

Author: Sheng Chen
Author: Xia Hong
Author: Chris Harris

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Library staff edit
Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×