Grey-box radial basis function modelling

Authors:
Sheng Chen;Xia Hong;Chris J. Harris
Affiliations:
School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK;School of Systems Engineering, University of Reading, Reading RG6 6AY, UK;School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
Venue:
Neurocomputing
Year:
2011

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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.