Grey-box radial basis function modelling

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• 作者：Sheng Chen^a ; ^{sqc@ecs.soton.ac.uk} ; Xia Hong^b ; ^{x.hong@reading.ac.uk} ; Chris J. Harris^a ; ^{cjh@ecs.soton.ac.uk} [Author vitae]
• 关键词：Data modelling ; Radial basis function network ; Black-box model ; Grey-box model ; Orthogonal least squares algorithm ; Symmetry ; Boundary value constraint
• 刊名：Neurocomputing
• 出版年：2011
• 出版时间：May 2011
• 年：2011
• 卷：74
• 期：10
• 页码：1564-1571
• 全文大小：1278 K

文摘

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.