{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004550132,0.000104244,0.0001097244,0.000170219,0.0003980952,0.00003457318,0.0002867027,0.0000142928,0.000004760964],"category_scores_gemma":[0.000007550436,0.00009171525,0.00002523255,0.0008497997,0.00009332706,0.0000351705,0.00005472271,0.00007320301,0.000005225213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000142426,"about_ca_system_score_gemma":0.00001405933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002021449,"about_ca_topic_score_gemma":2.975723e-7,"domain_scores_codex":[0.9990016,0.00001014727,0.0001614876,0.0002636476,0.0003233027,0.0002397765],"domain_scores_gemma":[0.999661,0.00006047404,0.00004935566,0.0001689483,0.00002242932,0.00003776121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005982814,0.00001317731,0.000009112905,0.00004292113,0.00000883011,0.000001285113,0.000109194,0.9597897,0.02033303,0.01758469,0.00008035444,0.002021739],"study_design_scores_gemma":[0.0001479,0.0001095846,0.00008965731,0.00001077237,0.00001190138,0.000008183143,0.0008876522,0.9408045,0.05576041,0.0001684337,0.001805724,0.0001952696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1229091,0.00007765184,0.8742722,0.00002004988,0.0001950679,0.0006574098,0.00002749155,0.0008613706,0.0009796619],"genre_scores_gemma":[0.9311351,7.836861e-7,0.06782797,0.00001078353,0.00002877072,0.0009560391,0.00000603082,0.00001714008,0.00001734494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.808226,"threshold_uncertainty_score":0.3740039,"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."}}