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Modelling and inverting complex-valued Wiener systems

Record type: Conference or Workshop Item (Paper)

We develop a complex-valued (CV) B-spline neural network approach for efficient identification and inversion of CV Wiener systems. The CV nonlinear static function in the Wiener system is represented using the tensor product of two univariate B-spline neural networks. With the aid of a least squares parameter initialisation, the Gauss-Newton algorithm effectively estimates the model parameters that include the CV linear dynamic model coefficients and B-spline neural network weights. The identification algorithm naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first order derivative recursions. An accurate inverse of the CV Wiener system is then obtained, in which the inverse of the CV nonlinear static function of the Wiener system is calculated efficiently using the Gaussian-Newton algorithm based on the estimated B-spline neural network model, with the aid of the De Boor recursions. The effectiveness of our approach for identification and inversion of CV Wiener systems is demonstrated using the application of digital predistorter design for high power amplifiers with memory

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Citation

Hong, Xia, Chen, Sheng and Harris, Chris J. (2012) Modelling and inverting complex-valued Wiener systems At International Joint Conference on Neural Networks (IJCNN 2012), Australia. 10 - 15 Jun 2012.

More information

e-pub ahead of print date: June 2012
Venue - Dates: International Joint Conference on Neural Networks (IJCNN 2012), Australia, 2012-06-10 - 2012-06-15
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 338820
URI: http://eprints.soton.ac.uk/id/eprint/338820
PURE UUID: 66fac5da-fbac-4b69-96a6-a15d0eabba36

Catalogue record

Date deposited: 17 May 2012 14:25
Last modified: 18 Jul 2017 05:57

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Contributors

Author: Xia Hong
Author: Sheng Chen
Author: Chris J. Harris

University divisions

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