Name
Abstract | ||
|---|---|---|
Fast QR decomposition RLS (FQRD-RLS) algorithms are well known for their good numerical properties and low computational complexity. However, the FQRD-RLS algorithms do not provide access to the filter weights, and so far their use has been limited to problems seeking an estimate of the output error signal. In this paper we present a novel technique to obtain the filter weights of the FQRD-RLS algorithm at any time instant. As a consequence, we extend the range of applications to include problems where explicit knowledge of the filter weights is required. The proposed weight extraction technique is tested in a system identification setup. The results verify our claim that the extracted coefficients of the FQRD-RLS algorithm are identical to those obtained by any RLS algorithm such as the inverse QRD-RLS algorithm. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICASSP.2006.1660718 | 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, Vols 1-13 |
Keywords | Field | DocType |
computational complexity,system identification,qr decomposition,recursive least squares,adaptive filters,error correction,explicit knowledge,feature extraction,robustness | Explicit knowledge,Computer science,Artificial intelligence,Adaptive filter,System identification,QR decomposition,Inverse,Mathematical optimization,Pattern recognition,Algorithm,Feature extraction,Recursive least squares filter,Computational complexity theory | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mobien Shoaib | 1 | 30 | 6.02 |
| Stefan Werner | 2 | 1545 | 124.74 |
| José Antonio Apolinário | 3 | 145 | 21.29 |
| Timo I. Laakso | 4 | 129 | 34.24 |