A Neural Approach to the Underdetermined-Order Recursive Least-Squares Adaptive Filtering

• Published in: Neural networks Vol. 10; no. 8; pp. 1523 - 1531
• Main Authors: Baykal, Buyurman, Constantinides, Anthony G.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.11.1997; Elsevier Science
• Subjects: Adaptive Filtering; Analog Adaptive Filter; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Connectionism. Neural networks; Exact sciences and technology; Neural Networks; Underdetermined Recursive Least Squares; Neural Networks; Adaptive Filtering; Underdetermined Recursive Least Squares; Analog Adaptive Filter; Adaptive filtering; Signal processing; Neural network; Least squares method; Recursive algorithm
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/S0893-6080(97)00045-2

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.