Grey-box radial basis function modelling
作者： Sheng Chen , Xia Hong , Chris J. Harris
DOI: 10.1016/J.NEUCOM.2011.01.023
关键词:
摘要: A fundamental principle in data modelling is to incorporate available a priori information regarding the underlying generating mechanism into process. We adopt this and consider grey-box radial basis function (RBF) capable of incorporating prior knowledge. Specifically, we show how explicitly two types knowledge: (i) exhibits known symmetric property, (ii) process obeys set given boundary value constraints. The class efficient orthogonal least squares regression algorithms can readily be applied without any modification construct parsimonious RBF models with enhanced generalisation capability.
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