{"id":"W4231343787","doi":"10.32920/ryerson.14664141.v1","title":"Minimum variance tuning of PI controllers using hybrid genetic algorithms","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Crossover; Variance (accounting); Genetic algorithm; Logarithm; Selection (genetic algorithm); Algorithm; Controller (irrigation); Control theory (sociology); PID controller; Mathematics; Fine-tuning; Mathematical optimization; Computer science; Control (management); Engineering; Temperature control; Control engineering; Artificial intelligence; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007637751,0.0004587806,0.0004984845,0.0007073093,0.0002633736,0.0007787317,0.0006483081,0.0006001341,0.000852825],"category_scores_gemma":[0.001875683,0.0002876327,0.0003678621,0.0004738674,0.0005084096,0.0003391317,0.0004121307,0.000466133,0.0001605152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005217243,"about_ca_system_score_gemma":0.0004343093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002486565,"about_ca_topic_score_gemma":0.002155805,"domain_scores_codex":[0.9996698,0.0001104203,0.0000124356,0.00004930314,0.0001267984,0.0000312546],"domain_scores_gemma":[0.9995441,0.000272543,0.00005427459,0.00003909636,0.00008015798,0.00000979859],"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":[0.0000474882,0.0000294424,0.0003466171,0.00002335489,0.00002707993,0.00002455017,0.00003258187,0.9503037,0.004327041,0.003534121,0.0001817713,0.04112228],"study_design_scores_gemma":[0.000008300897,0.00002158943,0.0001101201,0.000002509616,0.00000407267,0.000008087678,0.000003342377,0.9978788,0.0008354895,0.0009799562,0.0001448928,0.000002912889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07826909,0.000178062,0.9157687,0.00007960083,0.00002105803,0.00004418656,0.00001345245,0.0004052542,0.005220597],"genre_scores_gemma":[0.8219611,0.0000984162,0.175644,0.00004743735,0.00001332756,0.00009996202,0.00003306857,0.00005550253,0.002047222],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002486565,"threshold_uncertainty_score":0.004944205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01381062707555547,"score_gpt":0.2269548591863407,"score_spread":0.2131442321107853,"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."}}