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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), 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.

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More information

Published date: May 2011
Organisations: Southampton Wireless Group

Identifiers

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

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Date deposited: 09 May 2011 13:32
Last modified: 14 Mar 2024 09:51

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Contributors

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

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