Grey-box radial basis function modelling

Chen, S., Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298 and Harris, C. J. (2011) Grey-box radial basis function modelling. Neurocomputing, 74 (10). pp. 1564-1571. ISSN 0925-2312 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
URI	https://reading-clone.eprints-hosting.org/id/eprint/19943
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Uncontrolled Keywords	Data modelling; Radial basis function network; Black-box model; Grey-box model; Orthogonal least squares algorithm; Symmetry; Boundary value constraint
Publisher	Elsevier
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