{"id":"W4224309117","doi":"10.3390/app12094263","title":"Efficient Algorithms for Linear System Identification with Particular Symmetric Filters","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii","keywords":"Computer science; Algorithm; Impulse response; Kronecker product; System identification; Finite impulse response; Identification (biology); Wiener filter; Rank (graph theory); Adaptive filter; Filter (signal processing); Kronecker delta; Mathematical optimization; Mathematics; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001742102,0.001813852,0.001179498,0.001102901,0.0005463999,0.001326916,0.001400449,0.001575835,0.005136784],"category_scores_gemma":[0.005084688,0.0007223781,0.00138615,0.001249334,0.0007820317,0.001546842,0.001927194,0.002463708,0.003783194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006640386,"about_ca_system_score_gemma":0.001860057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00212487,"about_ca_topic_score_gemma":0.002785945,"domain_scores_codex":[0.9988456,0.0003616856,0.0001002682,0.0002250066,0.0003556312,0.0001117623],"domain_scores_gemma":[0.9985708,0.0007886371,0.0001327728,0.0002108859,0.0002668468,0.00003001858],"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":[0.0001386327,0.00009328203,0.0003894008,0.0003642148,0.0001224238,0.0001247146,0.0002250075,0.3390679,0.01401766,0.1052108,0.003702661,0.5365434],"study_design_scores_gemma":[0.00002063069,0.00004089179,0.0001013056,0.0000246688,0.00001409804,0.00007672727,0.00002544678,0.9657205,0.003169604,0.02695741,0.003828964,0.00001970064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003927768,0.00007406685,0.9990234,0.00001804288,0.00001080306,0.00001530447,0.00001086962,0.0001595745,0.0002951208],"genre_scores_gemma":[0.0375046,0.0004341228,0.958707,0.00006325127,0.00006330335,0.0002997795,0.0002213309,0.0001216484,0.00258494],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005136784,"threshold_uncertainty_score":0.01718426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02364441712817759,"score_gpt":0.2484389693626853,"score_spread":0.2247945522345077,"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."}}