{"id":"W2570958468","doi":"10.1049/iet-cta.2016.1033","title":"Hierarchical identification for multivariate Hammerstein systems by using the modified Kalman filter","year":2017,"lang":"en","type":"article","venue":"IET Control Theory and Applications","topic":"Control Systems and Identification","field":"Engineering","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Division of Graduate Education; Graduate Research and Innovation Projects of Jiangsu Province; National Natural Science Foundation of China","keywords":"Kalman filter; Control theory (sociology); Multivariate statistics; Identification (biology); Computer science; Mathematics; Control engineering; Artificial intelligence; Engineering; Control (management); Machine learning","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":[0.0009495135,0.000139187,0.0001856111,0.00003357952,0.001061954,0.0004920523,0.0003130947,0.00008430165,0.000003063219],"category_scores_gemma":[0.00006203033,0.0001101199,0.00006983768,0.00003541426,0.0001133861,0.000197594,0.0000178974,0.0001032488,0.000007767039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002838172,"about_ca_system_score_gemma":0.0000080398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006488625,"about_ca_topic_score_gemma":0.000007157271,"domain_scores_codex":[0.9990513,0.0001015679,0.0003301345,0.0002369101,0.00008956533,0.000190512],"domain_scores_gemma":[0.9986717,0.0003145134,0.0001633086,0.0007155539,0.00007223964,0.00006269537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009802022,0.00004495061,0.0000400693,0.0001117396,0.0002182413,1.362031e-7,0.0002425989,0.007669547,0.3952658,0.5743904,0.001008608,0.02090989],"study_design_scores_gemma":[0.001981734,0.00001316404,0.001463631,0.00003279388,0.0001992959,0.00000550076,0.0002002732,0.9452612,0.0009727235,0.02034246,0.02922133,0.0003058627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01776901,0.001017864,0.9776559,0.0004402841,0.0002670843,0.002077878,0.0002854257,0.00009835184,0.0003881648],"genre_scores_gemma":[0.9968328,0.00002068328,0.00003004973,0.00002696512,0.0003243386,0.002001094,0.00003960274,0.00002643096,0.0006980111],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9790638,"threshold_uncertainty_score":0.8167797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01798240579012227,"score_gpt":0.2606880554206816,"score_spread":0.2427056496305593,"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."}}