Sparse multi-output radial basis function network construction using combined locally regularised orthogonal least square and D-Optimality experimental design
Sparse multi-output radial basis function network construction using combined locally regularised orthogonal least square and D-Optimality experimental design
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.
139-146
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
March 2003
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
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.
Control Theory and Applications, IEE Proceedings, 150 (2), .
Abstract
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.
More information
Published date: March 2003
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 257350
URI: http://eprints.soton.ac.uk/id/eprint/257350
ISSN: 1350-2379
PURE UUID: 6e0f659a-9c24-434a-a5cb-a2c1e39491d7
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Date deposited: 16 Apr 2003
Last modified: 14 Mar 2024 05:56
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Contributors
Author:
S. Chen
Author:
X. Hong
Author:
C.J. Harris
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