{"id":"W2113479484","doi":"10.1109/acc.2006.1656574","title":"Nonlinear system identification using optimally selected Laguerre filter banks","year":2006,"lang":"en","type":"article","venue":"","topic":"Control Systems and Identification","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Laguerre polynomials; Nonlinear system; Laguerre's method; System identification; Finite impulse response; A priori and a posteriori; Control theory (sociology); Iterative method; Nonlinear system identification; Computer science; Impulse response; Mathematics; Filter (signal processing); Mathematical optimization; Nonlinear filter; Linear system; Algorithm; Identification (biology); Polynomial; Applied mathematics; Filter design; Orthogonal polynomials; Data modeling; Mathematical analysis; Physics; 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":[0.0001391512,0.0001245506,0.000144194,0.0001038512,0.00007277134,0.000157556,0.0001004207,0.00008546824,0.00004464255],"category_scores_gemma":[0.000006593189,0.0001246902,0.00004719289,0.0002760238,0.000007684342,0.0002120369,0.000008902795,0.00006980383,0.0002504959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001463613,"about_ca_system_score_gemma":0.00001346049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004516488,"about_ca_topic_score_gemma":0.0001466827,"domain_scores_codex":[0.9990546,0.00002141149,0.0004110227,0.0001773601,0.0001499018,0.0001857156],"domain_scores_gemma":[0.9994916,0.0000124395,0.0000552753,0.0002524603,0.0001566295,0.00003158369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005343506,0.00003124894,0.0008429479,0.0002438383,0.00004372443,0.000003854624,0.00004157594,0.07431456,0.9174418,0.00121729,0.004724793,0.001089031],"study_design_scores_gemma":[0.0002145725,0.000004087405,0.01330343,0.00003716063,0.0000253215,0.0000156053,0.0000372456,0.9701553,0.01427726,0.000004600664,0.001753047,0.0001723951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.842072,0.0002856027,0.1434309,0.00002411799,0.0008614689,0.0004229316,0.00002014487,0.001242582,0.01164033],"genre_scores_gemma":[0.995598,0.000001364026,0.002134742,0.000002605043,0.0003610395,0.00001941698,0.00009076826,0.00003338067,0.001758712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9031645,"threshold_uncertainty_score":0.5084719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007005613782481446,"score_gpt":0.1897344656720634,"score_spread":0.182728851889582,"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."}}