{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001233456,0.0003618252,0.000806958,0.0001445079,0.00003447995,0.00006472664,0.0002524987,0.0001998974,0.00006308957],"category_scores_gemma":[0.00005828729,0.0004198074,0.000171975,0.0001315696,0.00002991127,0.000106123,0.000174842,0.0003373004,0.00000241625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002025931,"about_ca_system_score_gemma":0.000102829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009614634,"about_ca_topic_score_gemma":0.000007319587,"domain_scores_codex":[0.9982266,0.00006271074,0.0007357558,0.0004103324,0.0002458409,0.0003187538],"domain_scores_gemma":[0.9988041,0.00007912037,0.0002290493,0.0005996311,0.0002064766,0.00008161916],"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":[0.000005373158,0.00000827732,0.00003293897,0.0002910328,0.0002139699,0.00002459907,0.00008353817,0.9826523,0.01474442,0.00001488667,0.00001911353,0.00190952],"study_design_scores_gemma":[0.0007512788,0.000007889565,0.00004784532,0.0003667202,0.00008839249,0.00002047044,0.00008933115,0.9962932,0.001828218,0.00008594274,0.00005140875,0.0003692407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01603329,0.003277766,0.9765826,0.000009937226,0.001511008,0.0005276711,0.00003204093,0.0002756143,0.001750016],"genre_scores_gemma":[0.7366287,0.0001431436,0.2627234,0.00001462218,0.0001961893,0.00003847224,0.00003873142,0.000093692,0.0001230951],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7205954,"threshold_uncertainty_score":0.9998254,"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."}}