{"id":"W2103171768","doi":"10.5539/cis.v7n3p58","title":"Design and Analysis of Bayesian Model Predictive Controller","year":2014,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; Shanghai Municipal Education Commission; National Natural Science Foundation of China","keywords":"Computer science; Model predictive control; Bayesian probability; Robustness (evolution); Nonlinear system; Control theory (sociology); Tracing; Controller (irrigation); Convergence (economics); Artificial intelligence; Control (management)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008798413,0.0007533091,0.0008897905,0.0004678388,0.0005391452,0.001045997,0.001336592,0.000987911,0.001421111],"category_scores_gemma":[0.001809028,0.0004257254,0.000421875,0.0003646669,0.0006293422,0.0007267548,0.0008500144,0.001054747,0.0003359149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009134511,"about_ca_system_score_gemma":0.001361789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007418033,"about_ca_topic_score_gemma":0.004259246,"domain_scores_codex":[0.9993292,0.00009751969,0.00002516671,0.0001453327,0.0003486405,0.00005417833],"domain_scores_gemma":[0.9994961,0.0001603961,0.00008051068,0.00002896834,0.0002161319,0.00001787786],"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":[0.00009072509,0.00004175044,0.000640152,0.0002069249,0.00005742065,0.0001369124,0.0001104602,0.8944777,0.007588332,0.02583921,0.001350065,0.06946034],"study_design_scores_gemma":[0.000006588918,0.0000203222,0.00008011076,0.000006144827,0.000006547626,0.00001340038,0.000003681888,0.9973809,0.0005664675,0.001405732,0.0005046679,0.000005442084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00487222,0.000353328,0.99015,0.0001414559,0.00003818602,0.00004093953,0.00002326231,0.0002360453,0.00414471],"genre_scores_gemma":[0.8992083,0.0007970498,0.09515718,0.0001767547,0.00007329931,0.0003598162,0.0001308538,0.0000554723,0.004041265],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007418033,"threshold_uncertainty_score":0.01474971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005219833978622062,"score_gpt":0.1955122030680294,"score_spread":0.1902923690894073,"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."}}