Sparse multi-output 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 multi-output radial basis function network construction using combined locally regularised orthogonal least square and D-Optimality experimental design. IEE Proc. Control Theory and Application, 150, (2), 139-146.
A new construction algorithm for multi-output radial basis function (RBF) network modelling is introduce by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized 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 generalization performance. The D-optimality design criterion further 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.
|Divisions:||Faculty of Physical and Applied Science > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||16 Apr 2003|
|Last Modified:||02 Mar 2012 11:57|
|Contributors:||Chen, S. (Author)
Hong, X. (Author)
Harris, C.J. (Author)
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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