Partial-update NLMS algorithms with data-selective updating
In this paper, we present mean-squared convergence analysis for the partial-update normalized least-mean square (PU-NLMS) algorithm with closed-form expressions for the case of white input signals. The formulae presented here are more accurate than the ones found in the literature for the PU-NLMS al...
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| Veröffentlicht in: | IEEE transactions on signal processing 2004-04, Vol.52 (4), p.938-949 |
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| creator | Werner, S. de Campos, M.L.R. Diniz, P.S.R. |
| description | In this paper, we present mean-squared convergence analysis for the partial-update normalized least-mean square (PU-NLMS) algorithm with closed-form expressions for the case of white input signals. The formulae presented here are more accurate than the ones found in the literature for the PU-NLMS algorithm. Thereafter, the ideas of the partial-update NLMS-type algorithms found in the literature are incorporated in the framework of set-membership filtering, from which data-selective NLMS-type algorithms with partial-update are derived. The new algorithms, referred to herein as the set-membership partial-update normalized least-mean square (SM-PU-NLMS) algorithms, combine the data-selective updating from set-membership filtering with the reduced computational complexity from partial updating. A thorough discussion of the SM-PU-NLMS algorithms follows, whereby we propose different update strategies and provide stability analysis and closed-form formulae for excess mean-squared error (MSE). Simulation results verify the analysis for the PU-NLMS algorithm and the good performance of the SM-PU-NLMS algorithms in terms of convergence speed, final misadjustment, and computational complexity. |
| doi_str_mv | 10.1109/TSP.2004.823483 |
| format | Article |
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The formulae presented here are more accurate than the ones found in the literature for the PU-NLMS algorithm. Thereafter, the ideas of the partial-update NLMS-type algorithms found in the literature are incorporated in the framework of set-membership filtering, from which data-selective NLMS-type algorithms with partial-update are derived. The new algorithms, referred to herein as the set-membership partial-update normalized least-mean square (SM-PU-NLMS) algorithms, combine the data-selective updating from set-membership filtering with the reduced computational complexity from partial updating. A thorough discussion of the SM-PU-NLMS algorithms follows, whereby we propose different update strategies and provide stability analysis and closed-form formulae for excess mean-squared error (MSE). Simulation results verify the analysis for the PU-NLMS algorithm and the good performance of the SM-PU-NLMS algorithms in terms of convergence speed, final misadjustment, and computational complexity.</description><identifier>ISSN: 1053-587X</identifier><identifier>EISSN: 1941-0476</identifier><identifier>DOI: 10.1109/TSP.2004.823483</identifier><identifier>CODEN: ITPRED</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Algorithm design and analysis ; Algorithms ; Analytical models ; Applied sciences ; Closed-form solution ; Complexity ; Computation ; Computational complexity ; Computational modeling ; Convergence ; Detection, estimation, filtering, equalization, prediction ; Error correction ; Exact sciences and technology ; Exact solutions ; Filtering ; Filtering algorithms ; Filtration ; Information, signal and communications theory ; Mathematical analysis ; Signal analysis ; Signal and communications theory ; Signal, noise ; Stability analysis ; Studies ; Telecommunications and information theory</subject><ispartof>IEEE transactions on signal processing, 2004-04, Vol.52 (4), p.938-949</ispartof><rights>2004 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2004</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c379t-277bd7647486b88f95b04c6c5a105f63a56c6d70028c64f5eb236bb5f52c814d3</citedby><cites>FETCH-LOGICAL-c379t-277bd7647486b88f95b04c6c5a105f63a56c6d70028c64f5eb236bb5f52c814d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1275668$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1275668$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=15586966$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Werner, S.</creatorcontrib><creatorcontrib>de Campos, M.L.R.</creatorcontrib><creatorcontrib>Diniz, P.S.R.</creatorcontrib><title>Partial-update NLMS algorithms with data-selective updating</title><title>IEEE transactions on signal processing</title><addtitle>TSP</addtitle><description>In this paper, we present mean-squared convergence analysis for the partial-update normalized least-mean square (PU-NLMS) algorithm with closed-form expressions for the case of white input signals. The formulae presented here are more accurate than the ones found in the literature for the PU-NLMS algorithm. Thereafter, the ideas of the partial-update NLMS-type algorithms found in the literature are incorporated in the framework of set-membership filtering, from which data-selective NLMS-type algorithms with partial-update are derived. The new algorithms, referred to herein as the set-membership partial-update normalized least-mean square (SM-PU-NLMS) algorithms, combine the data-selective updating from set-membership filtering with the reduced computational complexity from partial updating. A thorough discussion of the SM-PU-NLMS algorithms follows, whereby we propose different update strategies and provide stability analysis and closed-form formulae for excess mean-squared error (MSE). Simulation results verify the analysis for the PU-NLMS algorithm and the good performance of the SM-PU-NLMS algorithms in terms of convergence speed, final misadjustment, and computational complexity.</description><subject>Algorithm design and analysis</subject><subject>Algorithms</subject><subject>Analytical models</subject><subject>Applied sciences</subject><subject>Closed-form solution</subject><subject>Complexity</subject><subject>Computation</subject><subject>Computational complexity</subject><subject>Computational modeling</subject><subject>Convergence</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Error correction</subject><subject>Exact sciences and technology</subject><subject>Exact solutions</subject><subject>Filtering</subject><subject>Filtering algorithms</subject><subject>Filtration</subject><subject>Information, signal and communications theory</subject><subject>Mathematical analysis</subject><subject>Signal analysis</subject><subject>Signal and communications theory</subject><subject>Signal, noise</subject><subject>Stability analysis</subject><subject>Studies</subject><subject>Telecommunications and information theory</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2004</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kEtLAzEQgBdRsFbPHrwsgnraNs9JgicpvqBqoRW8hWyarVu23ZrsKv57U1soePA0A_PN60uSU4x6GCPVn4xHPYIQ60lCmaR7SQcrhjPEBOzHHHGacSneDpOjEOYIYcYUdJLrkfFNaaqsXU1N49Ln4dM4NdWs9mXzvgjpVwxprJgsuMrZpvx06S9aLmfHyUFhquBOtrGbvN7dTgYP2fDl_nFwM8wsFarJiBD5VAATTEIuZaF4jpgFy008qgBqOFiYCoSItMAK7nJCIc95wYmVmE1pN7nazF35-qN1odGLMlhXVWbp6jZoqQArIhWJ5OW_JJE8bkEygud_wHnd-mX8QktJgURrKEL9DWR9HYJ3hV75cmH8t8ZIr53r6FyvneuN89hxsR1rgjVV4c3SlmHXxrkEBRC5sw1XOud2ZSI4gKQ_UtmH9Q</recordid><startdate>20040401</startdate><enddate>20040401</enddate><creator>Werner, S.</creator><creator>de Campos, M.L.R.</creator><creator>Diniz, P.S.R.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The formulae presented here are more accurate than the ones found in the literature for the PU-NLMS algorithm. Thereafter, the ideas of the partial-update NLMS-type algorithms found in the literature are incorporated in the framework of set-membership filtering, from which data-selective NLMS-type algorithms with partial-update are derived. The new algorithms, referred to herein as the set-membership partial-update normalized least-mean square (SM-PU-NLMS) algorithms, combine the data-selective updating from set-membership filtering with the reduced computational complexity from partial updating. A thorough discussion of the SM-PU-NLMS algorithms follows, whereby we propose different update strategies and provide stability analysis and closed-form formulae for excess mean-squared error (MSE). Simulation results verify the analysis for the PU-NLMS algorithm and the good performance of the SM-PU-NLMS algorithms in terms of convergence speed, final misadjustment, and computational complexity.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TSP.2004.823483</doi><tpages>12</tpages></addata></record> |
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| subjects | Algorithm design and analysis Algorithms Analytical models Applied sciences Closed-form solution Complexity Computation Computational complexity Computational modeling Convergence Detection, estimation, filtering, equalization, prediction Error correction Exact sciences and technology Exact solutions Filtering Filtering algorithms Filtration Information, signal and communications theory Mathematical analysis Signal analysis Signal and communications theory Signal, noise Stability analysis Studies Telecommunications and information theory |
| title | Partial-update NLMS algorithms with data-selective updating |
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