A Neural Approach to the Underdetermined-Order Recursive Least-Squares Adaptive Filtering.
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Output type: Journal article
Author list: Constantinides, Baykal
Publisher: Elsevier
Publication year: 1997
Volume number: 10
Issue number: 8
Start page: 1523
End page: 1531
Number of pages: 9
ISSN: 0893-6080
Languages: English-Great Britain (EN-GB)
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Open access status: closed
Abstract
The incorporation of the neural architectures in adaptive filtering applications has been addressed in detail. In particular, the Underdetermined-Order Recursive Least-Squares (URLS) algorithm, which lies between the well-known Normalized Least Mean Square and Recursive Least Squares algorithms, is reformulated via a neural architecture. The response of the neural network is seen to be identical to that of the algorithmic approach. Together with the advantage of simple circuit realization, this neural network avoids the drawbacks of digital computation such as error propagation and matrix inversion, which is ill-conditioned in most cases. It is numerically attractive because the quadratic optimization problem performs an implicit matrix inversion. Also, the neural network offers the flexibility of easy alteration of the prediction order of the URLS algorithm which may be crucial in some applications. It is rather difficult to achieve in the digital implementation, as one would have to use Levinson recursions. The neural network can easily be integrated into a digital system through appropriate digital-to-analog and analog-to-digital converters.
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