{"id":"W2101100589","doi":"10.1109/cdc.1989.70216","title":"On the adaptive stabilization of linear stochastic systems with jump process parameters","year":2003,"lang":"en","type":"article","venue":"","topic":"Fuzzy Systems and Optimization","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Canadian Institute for Advanced Research","funders":"","keywords":"Controllability; Jump process; Riccati equation; Stochastic process; Set (abstract data type); Markov process; Mathematics; Nonlinear system; Process (computing); Linear system; Applied mathematics; Filter (signal processing); Control theory (sociology); Controller (irrigation); Computer science; Mathematical optimization; Jump; Control (management); Artificial intelligence; Mathematical analysis; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002973054,0.0001046408,0.0001676919,0.00004180551,0.00006052759,0.00001574913,0.00006714334,0.00004339329,0.00002644462],"category_scores_gemma":[0.000450482,0.00005369416,0.0000225339,0.0002246663,0.00003816693,0.00005737439,0.000003541039,0.00005426239,0.000003807028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000255641,"about_ca_system_score_gemma":0.00004800933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002296491,"about_ca_topic_score_gemma":0.00001441377,"domain_scores_codex":[0.9991627,0.0001066067,0.0002338041,0.0001325694,0.0002589917,0.0001052765],"domain_scores_gemma":[0.9988206,0.0005153198,0.0001840069,0.0002182179,0.0002358393,0.00002604015],"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":[0.00004870293,0.00009749942,0.00004686799,0.0001827267,0.00003860363,3.204485e-7,0.001098191,0.4463873,0.000009348551,0.5518921,0.0001919466,0.00000640134],"study_design_scores_gemma":[0.001321862,0.001464957,0.00002106034,0.001014898,0.0001419454,0.0000162753,0.01844006,0.9329759,0.002712922,0.04139821,0.00002066455,0.0004712304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2509671,0.00002238489,0.7399604,0.00002969052,0.00009926067,0.00125487,0.000008256545,0.00005481254,0.007603288],"genre_scores_gemma":[0.9958744,5.314009e-7,0.003663099,0.00001440336,0.000008305567,0.00005894765,0.000001799222,0.00002004956,0.00035847],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7449073,"threshold_uncertainty_score":0.2189584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05037982505636893,"score_gpt":0.2699970756815691,"score_spread":0.2196172506252002,"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."}}