Fully complex-valued radial basis function networks for orthogonal least squares regression

Chen, 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. doi: 10.1109/IJCNN.2008.4633759

Abstract/Summary

We 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.

Item Type	Conference or Workshop Item (Paper)
URI	https://reading-clone.eprints-hosting.org/id/eprint/14629
Item Type	Conference or Workshop Item
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Uncontrolled Keywords	CHANNEL EQUALIZATION, ALGORITHM, DESIGN
Publisher	IEEE
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