{"id":"W2121235768","doi":"10.1109/isscs.2009.5206117","title":"On the influence of the forgetting factor of the RLS adaptive filter in system identification","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Recursive least squares filter; Control theory (sociology); Forgetting; Adaptive filter; Filter (signal processing); Computer science; Kernel adaptive filter; Identification (biology); Noise (video); Algorithm; Filter design; Artificial intelligence; Computer vision","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":[0.0001342022,0.00008986969,0.0001019559,0.00003129195,0.00003186834,0.000006120255,0.0003773893,0.0000353161,0.0000035757],"category_scores_gemma":[0.0001020421,0.00004314877,0.00005038958,0.0001965619,0.00003915632,0.0000929455,0.0000418601,0.0001373113,0.000001369927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007872799,"about_ca_system_score_gemma":0.000005745861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001169519,"about_ca_topic_score_gemma":0.000008397499,"domain_scores_codex":[0.9993631,0.00003678889,0.0002614705,0.00008778698,0.0001481592,0.0001027427],"domain_scores_gemma":[0.9992813,0.0001289936,0.0001036789,0.000433574,0.0000449878,0.00000742703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001184293,0.00002008818,0.0008081728,0.00007098602,0.00001573503,2.638321e-7,0.0009064095,0.119441,0.7754748,0.1018519,0.0001449291,0.001253844],"study_design_scores_gemma":[0.00005081011,0.00002203571,0.2374102,0.0004477512,0.00000327933,7.480423e-7,0.0001605647,0.008981221,0.7502836,0.002539822,0.00002511709,0.00007485163],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9808247,0.000009447907,0.01732041,0.000121278,0.00005186646,0.0004112195,0.00001147783,0.0001123458,0.00113727],"genre_scores_gemma":[0.9990971,0.00000122298,0.0007765604,0.00003436621,0.000006944802,0.00001678234,1.473904e-7,0.000009175591,0.0000576619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.236602,"threshold_uncertainty_score":0.1759556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01329459171690364,"score_gpt":0.2184571048348317,"score_spread":0.2051625131179281,"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."}}