Grey-box radial basis function modelling: the art of incorporating prior knowledgeChen, S., Hong, X. 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. 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: 10.1109/SSP.2009.5278559 Abstract/SummaryA 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.
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