{"id":"W2044180188","doi":"10.1002/cjce.5450810515","title":"On‐line Tuning of Model Predictive Controllers Using Fuzzy Logic","year":2003,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fuzzy logic; Control theory (sociology); Diagonal; Computer science; Matrix (chemical analysis); Binary number; Constant (computer programming); Fuzzy control system; Self-tuning; Simple (philosophy); Model predictive control; Measure (data warehouse); Feature (linguistics); Algorithm; Mathematical optimization; Mathematics; Control (management); Artificial intelligence; Control engineering; Data mining; Engineering; PID controller; Temperature control","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":[0.001068847,0.0005875466,0.0006088176,0.0004406474,0.000461636,0.001168491,0.0008313199,0.0006392009,0.001412478],"category_scores_gemma":[0.002838705,0.0002973089,0.0002842376,0.0002547549,0.000544555,0.0004570457,0.0004273535,0.0007866604,0.0002658447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006591298,"about_ca_system_score_gemma":0.0006214689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005321105,"about_ca_topic_score_gemma":0.004170225,"domain_scores_codex":[0.9994623,0.0001419336,0.00002703858,0.00009235107,0.0002135248,0.00006287792],"domain_scores_gemma":[0.9989755,0.0005461007,0.0001422432,0.0001128786,0.0002033503,0.00001998499],"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":[0.0001885183,0.0001418488,0.0005123295,0.00009841482,0.00004520401,0.00008351895,0.00009989087,0.8580916,0.02003367,0.004133404,0.0008644237,0.1157072],"study_design_scores_gemma":[0.00001376631,0.00003043027,0.0001077234,0.000005979489,0.000005543239,0.000009761996,0.000004598251,0.9959289,0.002736261,0.0009004012,0.0002519629,0.000004608532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08815976,0.0002108167,0.9016016,0.0001594685,0.00006076571,0.00009233317,0.00002617192,0.001139931,0.008549229],"genre_scores_gemma":[0.9713822,0.00004282283,0.02761467,0.00004337045,0.000009753266,0.00004215105,0.00001270141,0.00002375299,0.0008287008],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005321105,"threshold_uncertainty_score":0.0105803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01437396954388934,"score_gpt":0.2007886852629065,"score_spread":0.1864147157190171,"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."}}