{"id":"W2029430854","doi":"10.1114/1.1385806","title":"Separable Least Squares Identification of Nonlinear Hammerstein Models: Application to Stretch Reflex Dynamics","year":2001,"lang":"en","type":"article","venue":"Annals of Biomedical Engineering","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":136,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; University of Calgary","funders":"","keywords":"Nonlinear system; Cascade; Least-squares function approximation; Non-linear least squares; Mathematics; System identification; Algorithm; Recursive least squares filter; Nonlinear system identification; Gaussian; Polynomial; Separable space; Applied mathematics; Linear model; Monte Carlo method; Control theory (sociology); Mathematical optimization; Computer science; Estimation theory; Adaptive filter; Data modeling; Artificial intelligence; Statistics; Engineering; Mathematical analysis","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":[0.0006420528,0.0007867403,0.0009457498,0.0002704877,0.0003632142,0.000560719,0.0005116852,0.0009675772,0.001263923],"category_scores_gemma":[0.003796089,0.0005114984,0.0006131302,0.0003713743,0.0003251319,0.0005870414,0.0006343129,0.001107491,0.0003640856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000267507,"about_ca_system_score_gemma":0.0006366231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004732794,"about_ca_topic_score_gemma":0.004975008,"domain_scores_codex":[0.9997916,0.00008472626,0.00001637384,0.00004478556,0.0000459838,0.00001663309],"domain_scores_gemma":[0.9989288,0.0007729966,0.00007806807,0.00008741624,0.0001137773,0.00001886284],"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":[0.0001719432,0.0001150883,0.000596861,0.0001606053,0.00009073514,0.0001142063,0.0001968522,0.8160081,0.01413935,0.005327575,0.0004539512,0.1626248],"study_design_scores_gemma":[0.000006458142,0.00001732339,0.0001228685,0.000002594358,0.000005554772,0.00001397255,0.00000830216,0.9970668,0.0008451986,0.001733959,0.0001727325,0.000004157813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03521478,0.000180282,0.9635877,0.00009380114,0.00002043428,0.00001852841,0.00002601576,0.0002966171,0.000561894],"genre_scores_gemma":[0.7494835,0.0004392118,0.2446097,0.00004962426,0.00003903799,0.0001120627,0.0001285916,0.0001460696,0.004992274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004732794,"threshold_uncertainty_score":0.009410501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03056156409942959,"score_gpt":0.3104491919823848,"score_spread":0.2798876278829552,"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."}}