Fully complex-valued radial basis function networks for orthogonal least squares regressionChen, S., Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298 and Harris, C.J. (2008) Fully complex-valued radial basis function networks for orthogonal least squares regression. In: International Joint Conference on Neural Networks 2008 (IJCNN), Hong Kong, China, https://doi.org/10.1109/IJCNN.2008.4633759. Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1109/IJCNN.2008.4633759 Abstract/SummaryWe consider a fully complex-valued radial basis function (RBF) network for regression application. The locally regularised orthogonal least squares (LROLS) algorithm with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF network models, is extended to the fully complex-valued RBF network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully complex-valued RBF network.
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