{"id":"W2139810527","doi":"10.1109/ccece.2009.5090257","title":"A set-membership affine projection algorithm with adaptive error bound","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Convergence (economics); Algorithm; Adaptive filter; Set (abstract data type); Computer science; Projection (relational algebra); Upper and lower bounds; Tracking error; Affine transformation; Tracking (education); Mathematics; Mathematical optimization; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005731174,0.0002021264,0.0001529014,0.0001005097,0.00004703867,0.00002742295,0.00009664213,0.00006525216,0.00008333116],"category_scores_gemma":[0.000005764048,0.0001698139,0.00002904015,0.0002362953,0.00003096086,0.0002943663,0.00001204065,0.000178009,0.00001923157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000150769,"about_ca_system_score_gemma":0.00001223869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001791715,"about_ca_topic_score_gemma":0.00004035959,"domain_scores_codex":[0.9992791,0.00001054419,0.000124879,0.0002054422,0.0001152035,0.0002647746],"domain_scores_gemma":[0.9996594,0.00001513618,0.00002281101,0.0001968028,0.00004880503,0.00005703375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004587762,0.0002378095,0.0001018098,0.0001090489,0.0003420689,0.0003075835,0.002752603,0.03362609,0.04768719,0.02543492,0.01312595,0.8758162],"study_design_scores_gemma":[0.002008255,0.004736329,0.004154821,0.0003892848,0.0000865145,0.0003796557,0.001725576,0.6785416,0.2581297,0.02337747,0.02386845,0.002602369],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01228474,0.00004314922,0.9484155,0.00007445621,0.00007028296,0.0004039925,0.000008523739,0.003636794,0.03506258],"genre_scores_gemma":[0.6569715,0.000006331653,0.3417695,0.00005289834,0.00009574051,0.0000369394,0.00000718157,0.00003626281,0.001023741],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8732138,"threshold_uncertainty_score":0.6924808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0262222829039665,"score_gpt":0.2539021764010584,"score_spread":0.2276798934970919,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}