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

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3 Author(s)
Sheng Chen ; School of Electronics and Computer Science, University of Southampton, SO17 1BJ, UK ; Chris J. Harris ; Xia Hong

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.

Published in:

2009 IEEE/SP 15th Workshop on Statistical Signal Processing

Date of Conference:

Aug. 31 2009-Sept. 3 2009