Accessibility navigation


Grey-box radial basis function modelling

Chen, S., Hong, X. and Harris, C. J. (2011) Grey-box radial basis function modelling. Neurocomputing, 74 (10). pp. 1564-1571. ISSN 0925-2312

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1016/j.neucom.2011.01.023

Abstract/Summary

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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:19943
Uncontrolled Keywords:Data modelling; Radial basis function network; Black-box model; Grey-box model; Orthogonal least squares algorithm; Symmetry; Boundary value constraint
Publisher:Elsevier

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation