{"id":"W2038311087","doi":"10.1016/j.isatra.2007.06.003","title":"A tuning algorithm for model predictive controllers based on genetic algorithms and fuzzy decision making","year":2007,"lang":"en","type":"article","venue":"ISA Transactions","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; Virtual Materials Group (Canada)","funders":"","keywords":"Model predictive control; Algorithm; Computer science; Process (computing); Genetic algorithm; Fine-tuning; Fuzzy logic; Key (lock); Variety (cybernetics); Range (aeronautics); MIMO; Control (management); Machine learning; Artificial intelligence; Engineering","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":[0.0009903762,0.000708156,0.001120418,0.0007359631,0.0006982517,0.0008839954,0.001005713,0.001314056,0.001891786],"category_scores_gemma":[0.002310775,0.0004258667,0.0005498556,0.0006721219,0.0005597534,0.0006331251,0.0005613225,0.0009781851,0.0004059534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006276667,"about_ca_system_score_gemma":0.0007831589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005918807,"about_ca_topic_score_gemma":0.004822109,"domain_scores_codex":[0.9997064,0.0000670365,0.0000149403,0.00005734734,0.000120838,0.0000334482],"domain_scores_gemma":[0.9995264,0.00025432,0.00004030175,0.00003513107,0.0001285783,0.00001537904],"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":[0.00007833423,0.00007126484,0.0002393968,0.00004502305,0.00005670956,0.00004030271,0.00005174526,0.776713,0.003201639,0.009539111,0.001177293,0.2087862],"study_design_scores_gemma":[0.00001565728,0.00001918612,0.00006909193,0.000004444843,0.000008481559,0.00001158936,0.000002337709,0.9968786,0.0005036084,0.002119456,0.0003622785,0.000005217802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006272435,0.0001173744,0.9914691,0.00006148162,0.00004904365,0.0000333446,0.00001188476,0.0003314087,0.001653915],"genre_scores_gemma":[0.4654654,0.0001641106,0.5310392,0.0001281827,0.00007018867,0.0002389748,0.00007713984,0.0001114401,0.002705257],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005918807,"threshold_uncertainty_score":0.0117687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009272913345053362,"score_gpt":0.2417208491788969,"score_spread":0.2324479358338436,"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."}}