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Proceedings Paper

Nonlinear classification and adaptive structures
Author(s): Colin F. N. Cowan; Peter M. Grant; Shang-Liang Chen; Gavin J. Gibson
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Paper Abstract

The main purpose of this paper is to examine a number of possible architectures for nonlinear adaptive filtering specifically related to adaptive equalisation. The approach taken proceeds by first reformulating the filtering process as a form of classification task in N dimensions. In the case of filtering the dimensionality is determined by the number of data samples in the filter data input vector. The task of classification then proceeds using a number of possible strategies i. e. the multilayer perceptron Volterra series modeling and cluster analysis. The techniques are evaluated in comparison with normal linear equalisation procedures.

Paper Details

Date Published: 1 November 1990
PDF: 11 pages
Proc. SPIE 1348, Advanced Signal Processing Algorithms, Architectures, and Implementations, (1 November 1990); doi: 10.1117/12.23465
Show Author Affiliations
Colin F. N. Cowan, Univ. of Edinburgh (United Kingdom)
Peter M. Grant, Univ. of Edinburgh (United Kingdom)
Shang-Liang Chen, Univ. of Edinburgh (United Kingdom)
Gavin J. Gibson, Univ. of Edinburgh (United Kingdom)

Published in SPIE Proceedings Vol. 1348:
Advanced Signal Processing Algorithms, Architectures, and Implementations
Franklin T. Luk, Editor(s)

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