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Grey-box radial basis function modelling: the art of incorporating prior knowledge

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Chen, S., Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298 and Harris, C.J. (2009) Grey-box radial basis function modelling: the art of incorporating prior knowledge. In: 15th Workshop on Statistical Signal Processing (SSP 2009), Cardiff, Wales, UK, https://doi.org/10.1109/SSP.2009.5278559.

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To link to this item DOI: 10.1109/SSP.2009.5278559

Abstract/Summary

A basic 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: the underlying data generating mechanism exhibits known symmetric property and the underlying process obeys a set of given boundary value constraints. The class of orthogonal least squares regression algorithms can readily be applied to construct parsimonious grey-box RBF models with enhanced generalisation capability.

Item Type:Conference or Workshop Item (Paper)
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:14631
Uncontrolled Keywords:least mean squares methods, radial basis function networks , grey-box RBF model, grey-box radial basis function, orthogonal least squares regression algorithm , Radial basis function network, boundary value constraint, grey-box modelling, symmetry
Publisher:IEEE
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