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Complex-valued radial basis function networks

Complex-valued radial basis function networks
Complex-valued radial basis function networks
We propose a novel complex radial basis function
(RBF) network. The network has complex centres and weights but the response of its hidden nodes remains real. Several leaming algorithms for the existing real RBF network are extended to this complex network. The proposed network is capable of generating complicated nonlinear decision surface or approximating an arbitrary nonlinear function in multidimensional complex space and it provides a powerful tool for nonlinear signal processing involving complex
signals. This is demonstrated using two practical
applications to communication systems. The first case considers the equalisation of time-dispersive communication channels, and we show that the underlying Bayesian solution has an identical structure to the complex RBF network. In the second case, we use the complex RBF network to model nonlinear channels, and this application is typically found in channel estimation and echo cancellation involving nonlinear distortion.
148-152
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Grant, P. M.
e527fff4-da0f-4bc4-91cf-eed522070300
McLaughlin, S.
d8651585-025f-4ea9-bd15-cef87f323624
Mulgrew, B.
95a3fbda-7de2-4583-b1f2-0a54a69b414a
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Grant, P. M.
e527fff4-da0f-4bc4-91cf-eed522070300
McLaughlin, S.
d8651585-025f-4ea9-bd15-cef87f323624
Mulgrew, B.
95a3fbda-7de2-4583-b1f2-0a54a69b414a

Chen, S., Grant, P. M., McLaughlin, S. and Mulgrew, B. (1993) Complex-valued radial basis function networks. 3rd IEE International Conference on Artificial Neural Networks, Brighton, United Kingdom. pp. 148-152 .

Record type: Conference or Workshop Item (Paper)

Abstract

We propose a novel complex radial basis function
(RBF) network. The network has complex centres and weights but the response of its hidden nodes remains real. Several leaming algorithms for the existing real RBF network are extended to this complex network. The proposed network is capable of generating complicated nonlinear decision surface or approximating an arbitrary nonlinear function in multidimensional complex space and it provides a powerful tool for nonlinear signal processing involving complex
signals. This is demonstrated using two practical
applications to communication systems. The first case considers the equalisation of time-dispersive communication channels, and we show that the underlying Bayesian solution has an identical structure to the complex RBF network. In the second case, we use the complex RBF network to model nonlinear channels, and this application is typically found in channel estimation and echo cancellation involving nonlinear distortion.

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More information

Published date: 1993
Additional Information: 3rd IEE International Conference on Artificial Neural Networks (Brighton, UK), 1993. Event Dates: 1993 Organisation: IEE
Venue - Dates: 3rd IEE International Conference on Artificial Neural Networks, Brighton, United Kingdom, 1993-01-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 251118
URI: http://eprints.soton.ac.uk/id/eprint/251118
PURE UUID: b127387e-fde2-4961-848b-f7b67faa492a

Catalogue record

Date deposited: 12 Oct 1999
Last modified: 03 Feb 2022 17:49

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Contributors

Author: S. Chen
Author: P. M. Grant
Author: S. McLaughlin
Author: B. Mulgrew

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