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- Publisher Website: 10.1109/TCSII.2012.2184374
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Article: A New State-Regularized QRRLS Algorithm with a Variable Forgetting Factor
Title | A New State-Regularized QRRLS Algorithm with a Variable Forgetting Factor |
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Authors | |
Keywords | Adaptive filters QR decomposition (QRD) recursive least squares (RLS) variable forgetting factor (VFF) variable regularization |
Issue Date | 2012 |
Publisher | IEEE. The Journal's web site is located at http://tcas2.polito.it/ |
Citation | IEEE Transactions on Circuits and Systems Part 2: Express Briefs, 2012, v. 59 n. 3, p. 183-187 How to Cite? |
Abstract | This brief proposes a new state-regularized (SR) and QR-decomposition-based (QRD) recursive least squares (RLS) adaptive filtering algorithm with a variable forgetting factor (VFF). It employs the estimated coefficients as prior information to minimize the exponentially weighted observation error, which leads to reduced variance over a conventional RLS algorithm and reduced bias over an L2-regularized RLS algorithm. To improve the tracking performance, a new measure of convergence status is introduced in controlling the forgetting factor. Consequently, the resultant SR-VFF-RLS algorithm stabilizes the update and adaptively selects the number of measurements by means of the VFF. Improved tracking performance, steady-state mean-square error, and robustness to power-varying inputs over conventional RLS algorithms can be achieved. Furthermore, the proposed algorithm can be implemented using QRD, which leads to a lower roundoff error and more efficient hardware realization than the direct implementation. The effectiveness of the proposed algorithm is demonstrated by computer simulations. |
Persistent Identifier | http://hdl.handle.net/10722/189091 |
ISSN | 2023 Impact Factor: 4.0 2023 SCImago Journal Rankings: 1.523 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chan, SC | en_US |
dc.contributor.author | Chu, YJ | en_US |
dc.date.accessioned | 2013-09-17T14:24:59Z | - |
dc.date.available | 2013-09-17T14:24:59Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.citation | IEEE Transactions on Circuits and Systems Part 2: Express Briefs, 2012, v. 59 n. 3, p. 183-187 | en_US |
dc.identifier.issn | 1549-7747 | - |
dc.identifier.uri | http://hdl.handle.net/10722/189091 | - |
dc.description.abstract | This brief proposes a new state-regularized (SR) and QR-decomposition-based (QRD) recursive least squares (RLS) adaptive filtering algorithm with a variable forgetting factor (VFF). It employs the estimated coefficients as prior information to minimize the exponentially weighted observation error, which leads to reduced variance over a conventional RLS algorithm and reduced bias over an L2-regularized RLS algorithm. To improve the tracking performance, a new measure of convergence status is introduced in controlling the forgetting factor. Consequently, the resultant SR-VFF-RLS algorithm stabilizes the update and adaptively selects the number of measurements by means of the VFF. Improved tracking performance, steady-state mean-square error, and robustness to power-varying inputs over conventional RLS algorithms can be achieved. Furthermore, the proposed algorithm can be implemented using QRD, which leads to a lower roundoff error and more efficient hardware realization than the direct implementation. The effectiveness of the proposed algorithm is demonstrated by computer simulations. | - |
dc.language | eng | en_US |
dc.publisher | IEEE. The Journal's web site is located at http://tcas2.polito.it/ | - |
dc.relation.ispartof | IEEE Transactions on Circuits and Systems Part 2: Express Briefs | en_US |
dc.subject | Adaptive filters | - |
dc.subject | QR decomposition (QRD) | - |
dc.subject | recursive least squares (RLS) | - |
dc.subject | variable forgetting factor (VFF) | - |
dc.subject | variable regularization | - |
dc.title | A New State-Regularized QRRLS Algorithm with a Variable Forgetting Factor | en_US |
dc.type | Article | en_US |
dc.identifier.email | Chan, SC: ascchan@hkucc.hku.hk | en_US |
dc.identifier.email | Chu, Y: yjchu@eee.hku.hk | en_US |
dc.identifier.authority | Chan, SC=rp00094 | en_US |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TCSII.2012.2184374 | - |
dc.identifier.scopus | eid_2-s2.0-84858958784 | - |
dc.identifier.hkuros | 225093 | en_US |
dc.identifier.volume | 59 | en_US |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 183 | en_US |
dc.identifier.epage | 187 | en_US |
dc.identifier.eissn | 1558-3791 | - |
dc.identifier.isi | WOS:000302102400011 | - |
dc.identifier.issnl | 1549-7747 | - |