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Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental design

Chen, S., Hong, X. and Harris, C. J. (2003) Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental design. IEE Proceedings-Control Theory and Applications, 150 (2). pp. 139-146. ISSN 1350-2379

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To link to this item DOI: 10.1049/ip-cta:20030253

Abstract/Summary

A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced by combining a locally regularised orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximised model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious RBF network model with excellent generalisation performance. The D-optimality design criterion enhances the model efficiency and robustness. A further advantage of the combined approach is that the user only needs to specify a weighting for the D-optimality cost in the combined RBF model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:15164
Uncontrolled Keywords:NON-LINEAR SYSTEMS, ALGORITHM, IDENTIFICATION, REGRESSION
Publisher:IET

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