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Fully complex-valued radial basis function networks: orthogonal least squares regression and classification

Fully complex-valued radial basis function networks: orthogonal least squares regression and classification
Fully complex-valued radial basis function networks: orthogonal least squares regression and classification
We consider a fully complex-valued radial basis function (RBF) network for regression and classification applications. For regression problems, the locally regularised orthogonal least squares (LROLS) algorithm aided with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF models, is extended to the fully complex-valued RBF (CVRBF) 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 CVRBF network. The proposed fully CVRBF network is also applied to four-class classification problems that are typically encountered in communication systems. A complex-valued orthogonal forward selection algorithm based on the multi-class Fisher ratio of class separability measure is derived for constructing sparse CVRBF classifiers that generalise well. The effectiveness of the proposed algorithm is demonstrated using the example of nonlinear beamforming for multipleantenna aided communication systems that employ complex-valued quadrature phase shift keying modulation scheme.
0925-2312
3421–3433
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e2869895-2015-4f79-a624-f994027ed12a
Harris, Chris J.
dc305347-9cb2-4621-b42f-3f9950116e0d
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e2869895-2015-4f79-a624-f994027ed12a
Harris, Chris J.
dc305347-9cb2-4621-b42f-3f9950116e0d
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Chen, Sheng, Hong, Xia, Harris, Chris J. and Hanzo, Lajos (2008) Fully complex-valued radial basis function networks: orthogonal least squares regression and classification. Neurocomputing, 71 (16-18), 3421–3433.

Record type: Article

Abstract

We consider a fully complex-valued radial basis function (RBF) network for regression and classification applications. For regression problems, the locally regularised orthogonal least squares (LROLS) algorithm aided with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF models, is extended to the fully complex-valued RBF (CVRBF) 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 CVRBF network. The proposed fully CVRBF network is also applied to four-class classification problems that are typically encountered in communication systems. A complex-valued orthogonal forward selection algorithm based on the multi-class Fisher ratio of class separability measure is derived for constructing sparse CVRBF classifiers that generalise well. The effectiveness of the proposed algorithm is demonstrated using the example of nonlinear beamforming for multipleantenna aided communication systems that employ complex-valued quadrature phase shift keying modulation scheme.

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Published date: 20 September 2008
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 266694
URI: http://eprints.soton.ac.uk/id/eprint/266694
ISSN: 0925-2312
PURE UUID: 7ab9d123-6b03-4b53-9401-00c0972955d8
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 23 Sep 2008 08:32
Last modified: 22 Apr 2021 01:32

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