{"id":"W1935137508","doi":"10.1109/ssap.1998.739357","title":"Adaptive system identification using interior point optimization","year":2002,"lang":"en","type":"article","venue":"","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Interior point method; Convergence (economics); Adaptive filter; Computer science; Point (geometry); Identification (biology); Filter (signal processing); Algorithm; Optimization problem; Mathematical optimization; Iterative method; System identification; Control theory (sociology); Mathematics; Artificial intelligence; Data modeling","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":[0.001162876,0.0009948954,0.001151916,0.0005893697,0.0003772734,0.0007856947,0.0009882598,0.001268258,0.002852953],"category_scores_gemma":[0.002735056,0.00059066,0.0008170074,0.0004910703,0.001061267,0.001063967,0.001251754,0.00205767,0.001318311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004071469,"about_ca_system_score_gemma":0.0007944691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001446614,"about_ca_topic_score_gemma":0.00125022,"domain_scores_codex":[0.999453,0.0001851898,0.00002531692,0.00008732976,0.0002102466,0.00003896826],"domain_scores_gemma":[0.9991735,0.0004326096,0.00009699681,0.00009413634,0.0001732878,0.00002945365],"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":[0.00005984067,0.00003526494,0.0002800433,0.0001050523,0.00006702688,0.00005886014,0.0001200199,0.7898096,0.007060698,0.03514136,0.001553854,0.1657083],"study_design_scores_gemma":[0.000007065847,0.0000152749,0.00001926776,0.000004989641,0.000003179983,0.00001418199,0.000002634255,0.9942853,0.0009135962,0.003561013,0.001168296,0.000005155654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003331206,0.00001918992,0.9993275,0.00001239417,0.000007965277,0.000003944489,0.000002152339,0.00008607759,0.0002075682],"genre_scores_gemma":[0.0739481,0.000143892,0.9232561,0.0000661831,0.00004451318,0.0001676136,0.00005358367,0.0001206933,0.002199316],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002852953,"threshold_uncertainty_score":0.009544075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03821709954811816,"score_gpt":0.2393518126624207,"score_spread":0.2011347131143026,"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."}}