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Proceedings Paper

Adaptive algorithms for bilinear filtering
Author(s): V. John Mathews; Junghsi Lee
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Paper Abstract

This paper presents an overview of adaptive nonlinear filters equipped with bilinear system models. Bilinear filters are recursive nonlinear systems that belong to the class of polynomial systems. Because of the feedback structure, such models are able to represent many nonlinear systems efficiently. The paper first describe stochastic gradient adaptive bilinear filters. The class of recursive least-squares adaptive bilinear filters are discussed next. Stability issues associated with bilinear system models and adaptive bilinear filters are also discussed in the paper. The paper concludes with an experimental comparison of the performance of linear, truncated second-order Volterra, and bilinear system models in a nonlinear channel equalization problem.

Paper Details

Date Published: 28 October 1994
PDF: 11 pages
Proc. SPIE 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V, (28 October 1994); doi: 10.1117/12.190846
Show Author Affiliations
V. John Mathews, Univ. of Utah (United States)
Junghsi Lee, Industrial Technology Research Institute (Taiwan)


Published in SPIE Proceedings Vol. 2296:
Advanced Signal Processing: Algorithms, Architectures, and Implementations V
Franklin T. Luk, Editor(s)

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