{"id":"W2147632457","doi":"10.1109/cdc.1989.70542","title":"A state space approach to minimum variance control of multivariable ARMAX systems","year":2003,"lang":"en","type":"article","venue":"","topic":"Stability and Control of Uncertain Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Multivariable calculus; Minimum-variance unbiased estimator; Mathematics; State space; Variance (accounting); Control theory (sociology); Equivalence (formal languages); Algebraic Riccati equation; State (computer science); Algebraic number; Optimal control; Riccati equation; Control (management); Applied mathematics; Computer science; Mathematical optimization; Statistics; Pure mathematics; Algorithm; Mathematical analysis; Artificial intelligence; Engineering; Control engineering; Mean squared error; Differential equation","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":[0.0004288093,0.000492466,0.0004670882,0.0003676218,0.0003786211,0.001005664,0.0005338732,0.0005893295,0.002486229],"category_scores_gemma":[0.001033296,0.0002206566,0.0003698316,0.0004664944,0.0007192214,0.000717999,0.0007714285,0.000875434,0.0002912787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000491133,"about_ca_system_score_gemma":0.0004705287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002477443,"about_ca_topic_score_gemma":0.001559839,"domain_scores_codex":[0.99962,0.0001353105,0.00001712952,0.00005827028,0.000137833,0.00003140291],"domain_scores_gemma":[0.9997528,0.0001145855,0.00004382529,0.00001719668,0.00006119577,0.00001044505],"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":[0.00005286204,0.00003355466,0.0001759269,0.0001107431,0.00003413746,0.00009524872,0.0001411889,0.7140186,0.006589908,0.2329972,0.001082058,0.04466859],"study_design_scores_gemma":[0.000007287578,0.00005230808,0.0001002088,0.000008383999,0.000006510886,0.00001380483,0.00001245441,0.9510838,0.0008153107,0.0461247,0.001765678,0.000009538027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006017242,0.0002868392,0.9886108,0.0001806633,0.00003648477,0.00001253161,0.00002071367,0.000109846,0.004724829],"genre_scores_gemma":[0.9367999,0.0007843848,0.05442472,0.0001327427,0.000168599,0.0001153224,0.00007097902,0.00004846076,0.007454729],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002486229,"threshold_uncertainty_score":0.008317292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00976711696646392,"score_gpt":0.1929067888223089,"score_spread":0.1831396718558449,"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."}}