{"id":"https://openalex.org/W2098981768","doi":"https://doi.org/10.1109/tsp.2011.2106123","title":"Proportionate-Type Normalized Least Mean Square Algorithms With Gain Allocation Motivated by Mean-Square-Error Minimization for White Input","display_name":"Proportionate-Type Normalized Least Mean Square Algorithms With Gain Allocation Motivated by Mean-Square-Error Minimization for White Input","publication_year":2011,"publication_date":"2011-01-13","ids":{"openalex":"https://openalex.org/W2098981768","doi":"https://doi.org/10.1109/tsp.2011.2106123","mag":"2098981768"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2011.2106123","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2011.2106123","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037299270","display_name":"Kevin Wagner","orcid":"https://orcid.org/0000-0003-2843-7143"},"institutions":[{"id":"https://openalex.org/I4210145244","display_name":"Radar (United States)","ror":"https://ror.org/05c5a2504","country_code":"US","type":"company","lineage":["https://openalex.org/I4210145244"]},{"id":"https://openalex.org/I1288214837","display_name":"United States Naval Research Laboratory","ror":"https://ror.org/04d23a975","country_code":"US","type":"facility","lineage":["https://openalex.org/I1288214837","https://openalex.org/I1330347796","https://openalex.org/I175003984","https://openalex.org/I3130687028"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kevin Wagner","raw_affiliation_strings":["Radar Division, Naval Research Laboratory, Washington, DC, USA","[Radar Div., Naval Res. Lab., Washington, DC, USA]"],"affiliations":[{"raw_affiliation_string":"Radar Division, Naval Research Laboratory, Washington, DC, USA","institution_ids":["https://openalex.org/I1288214837","https://openalex.org/I4210145244"]},{"raw_affiliation_string":"[Radar Div., Naval Res. Lab., Washington, DC, USA]","institution_ids":["https://openalex.org/I1288214837"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042973603","display_name":"Milo\u0161 Doroslova\u010dki","orcid":"https://orcid.org/0000-0003-4101-3249"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Milos Doroslovacki","raw_affiliation_strings":["George Washington University, Washington, DC, USA","George Washington University Washington DC USA"],"affiliations":[{"raw_affiliation_string":"George Washington University, Washington, DC, USA","institution_ids":["https://openalex.org/I193531525"]},{"raw_affiliation_string":"George Washington University Washington DC USA","institution_ids":["https://openalex.org/I193531525"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5037299270"],"corresponding_institution_ids":["https://openalex.org/I1288214837","https://openalex.org/I4210145244"],"apc_list":null,"apc_paid":null,"fwci":3.5299,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.92713561,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"59","issue":"5","first_page":"2410","last_page":"2415"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.7153400778770447},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6563838720321655},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5743458271026611},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.5318441390991211},{"id":"https://openalex.org/keywords/minimum-mean-square-error","display_name":"Minimum mean square error","score":0.5248358845710754},{"id":"https://openalex.org/keywords/square","display_name":"Square (algebra)","score":0.5119456052780151},{"id":"https://openalex.org/keywords/least-mean-squares-filter","display_name":"Least mean squares filter","score":0.488810271024704},{"id":"https://openalex.org/keywords/standard-deviation","display_name":"Standard deviation","score":0.4182009696960449},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.40324944257736206},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.33403509855270386},{"id":"https://openalex.org/keywords/adaptive-filter","display_name":"Adaptive filter","score":0.2758963108062744},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2505291998386383}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.7153400778770447},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6563838720321655},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5743458271026611},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.5318441390991211},{"id":"https://openalex.org/C90652560","wikidata":"https://www.wikidata.org/wiki/Q11091747","display_name":"Minimum mean square error","level":3,"score":0.5248358845710754},{"id":"https://openalex.org/C135692309","wikidata":"https://www.wikidata.org/wiki/Q111124","display_name":"Square (algebra)","level":2,"score":0.5119456052780151},{"id":"https://openalex.org/C32617633","wikidata":"https://www.wikidata.org/wiki/Q1426666","display_name":"Least mean squares filter","level":3,"score":0.488810271024704},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.4182009696960449},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.40324944257736206},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.33403509855270386},{"id":"https://openalex.org/C102248274","wikidata":"https://www.wikidata.org/wiki/Q168388","display_name":"Adaptive filter","level":2,"score":0.2758963108062744},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2505291998386383},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2011.2106123","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2011.2106123","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W87496662","https://openalex.org/W1492221128","https://openalex.org/W1547069917","https://openalex.org/W2051335509","https://openalex.org/W2099464104","https://openalex.org/W2105591650","https://openalex.org/W2129629082","https://openalex.org/W2133524265","https://openalex.org/W2135969423","https://openalex.org/W2143643945","https://openalex.org/W2152264220","https://openalex.org/W2163732756","https://openalex.org/W3145321633","https://openalex.org/W4235295823","https://openalex.org/W4243330440"],"related_works":["https://openalex.org/W4252337044","https://openalex.org/W118630527","https://openalex.org/W2102148524","https://openalex.org/W38289631","https://openalex.org/W2592886773","https://openalex.org/W2922142213","https://openalex.org/W2075897667","https://openalex.org/W2907746047","https://openalex.org/W3125536267","https://openalex.org/W2274645452"],"abstract_inverted_index":{"In":[0,22],"the":[1,38,69,84,92,108,124,132,141],"past,":[2],"ad":[3],"hoc":[4],"methods":[5],"have":[6],"been":[7],"used":[8],"to":[9,107],"choose":[10],"gains":[11,33],"in":[12],"proportionate-type":[13,57],"normalized":[14,58],"least":[15,59],"mean-square":[16,39,60,85,93,136],"algorithms":[17,80,104,113],"without":[18],"strong":[19],"theoretical":[20,26,70],"under-pinnings.":[21],"this":[23,52],"correspondence,":[24],"a":[25,49,54],"framework":[27],"and":[28,119,134],"motivation":[29],"for":[30],"adaptively":[31],"choosing":[32],"is":[34,62,73,127],"presented,":[35],"such":[36],"that":[37],"error":[40],"will":[41],"be":[42],"minimized":[43],"at":[44],"any":[45],"given":[46],"time.":[47],"As":[48],"result":[50],"of":[51,68,78,83,100,110,145],"approach,":[53],"new":[55,102],"optimal":[56,71],"algorithm":[61,72],"introduced.":[63],"A":[64],"computationally":[65],"simplified":[66],"version":[67],"derived":[74],"as":[75,138,140],"well.":[76],"Both":[77],"these":[79,101],"require":[81],"knowledge":[82],"weight":[86,94],"deviations.":[87],"Feasible":[88],"implementations,":[89],"which":[90],"estimate":[91],"deviations,":[95],"are":[96,105],"presented.":[97],"The":[98],"performance":[99,109],"feasible":[103],"compared":[106],"standard":[111],"adaptive":[112],"when":[114,123],"operating":[115],"with":[116],"sparse,":[117],"non-sparse,":[118],"time-varying":[120],"impulse":[121],"responses,":[122],"input":[125],"signal":[126],"white.":[128],"Specifically,":[129],"we":[130],"consider":[131],"transient":[133],"steady-state":[135],"errors":[137],"well":[139],"overall":[142],"computational":[143],"complexity":[144],"each":[146],"algorithm.":[147]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":4},{"year":2012,"cited_by_count":2}],"updated_date":"2026-03-05T09:29:38.588285","created_date":"2025-10-10T00:00:00"}