The University of Southampton
University of Southampton Institutional Repository

Complex-valued B-spline neural networks for modeling and inverting Hammerstein systems

Complex-valued B-spline neural networks for modeling and inverting Hammerstein systems
Complex-valued B-spline neural networks for modeling and inverting Hammerstein systems
Many communication signal processing applications involve modeling and inverting complex-valued (CV) Hammerstein systems. We develop a new CV B-spline neural network approach for efficient identification of the CV Hammerstein system and effective inversion of the estimated CV Hammerstein model. In particular, the CV nonlinear static function in the Hammerstein system is represented using the tensor product from two univariate B-spline neural networks. An efficient alternating least squares estimation method is adopted for identifying the CV linear dynamic model’s coefficients and the CV B-spline neural network’s weights, which yields the closed-form solutions for both the linear dynamic model’s coefficients and the B-spline neural network’s weights, and this estimation process is guaranteed to converge very fast to a unique minimum solution. Furthermore, an accurate inversion of the CV Hammerstein system can readily be obtained using the estimated model. In particular, the inversion of the CV nonlinear static function in the Hammerstein system can be calculated effectively using a Gaussian Newton algorithm, which naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first-order derivative recursions. The effectiveness of our approach is demonstrated using the application to equalization of Hammerstein channels.
2162-237X
1673-1685
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Junbin, Gao
1568cc52-ea00-40df-948e-4a17b9eb931f
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Junbin, Gao
1568cc52-ea00-40df-948e-4a17b9eb931f
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Chen, Sheng, Hong, Xia, Junbin, Gao and Harris, Chris J. (2014) Complex-valued B-spline neural networks for modeling and inverting Hammerstein systems. IEEE Transactions on Neural Networks and Learning Systems, 25 (9), 1673-1685. (doi:10.1109/TNNLS.2014.2298535).

Record type: Article

Abstract

Many communication signal processing applications involve modeling and inverting complex-valued (CV) Hammerstein systems. We develop a new CV B-spline neural network approach for efficient identification of the CV Hammerstein system and effective inversion of the estimated CV Hammerstein model. In particular, the CV nonlinear static function in the Hammerstein system is represented using the tensor product from two univariate B-spline neural networks. An efficient alternating least squares estimation method is adopted for identifying the CV linear dynamic model’s coefficients and the CV B-spline neural network’s weights, which yields the closed-form solutions for both the linear dynamic model’s coefficients and the B-spline neural network’s weights, and this estimation process is guaranteed to converge very fast to a unique minimum solution. Furthermore, an accurate inversion of the CV Hammerstein system can readily be obtained using the estimated model. In particular, the inversion of the CV nonlinear static function in the Hammerstein system can be calculated effectively using a Gaussian Newton algorithm, which naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first-order derivative recursions. The effectiveness of our approach is demonstrated using the application to equalization of Hammerstein channels.

Text
TNNLS2014-9.pdf - Version of Record
Restricted to Repository staff only
Request a copy

More information

Published date: 15 August 2014
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 368122
URI: http://eprints.soton.ac.uk/id/eprint/368122
ISSN: 2162-237X
PURE UUID: d5a17acf-08cf-4c58-a846-e05a8679acb2

Catalogue record

Date deposited: 21 Aug 2014 17:01
Last modified: 25 Nov 2019 20:41

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×