{"id":"W1886985216","doi":"10.1109/iecon.2001.975605","title":"Fuzzy model reference learning control of induction motor via genetic algorithms","year":2002,"lang":"en","type":"article","venue":"","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Induction motor; Control theory (sociology); Computer science; Fuzzy control system; Fuzzy logic; Genetic algorithm; Controller (irrigation); Field (mathematics); Control engineering; Algorithm; Adaptive control; Control (management); Artificial intelligence; Engineering; Machine learning; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001625895,0.0001219254,0.0002214508,0.00007445734,0.00006548659,0.00004377793,0.0004763844,0.0000902914,0.00001660858],"category_scores_gemma":[0.00002665298,0.00009930301,0.0000521564,0.0002147074,0.00003217717,0.0002395562,0.00005911312,0.0001534606,0.00008801369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002594603,"about_ca_system_score_gemma":0.00001725395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009808478,"about_ca_topic_score_gemma":0.000002294298,"domain_scores_codex":[0.9987838,0.0000886655,0.0003001291,0.0003092205,0.0002851023,0.0002330791],"domain_scores_gemma":[0.9992521,0.00004205633,0.0001353098,0.00034381,0.000149415,0.00007732072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000283896,0.000304564,0.001371349,0.00005953105,0.0001161985,0.00001779701,0.001540294,0.1341948,0.07331613,0.161811,0.0007276114,0.6265123],"study_design_scores_gemma":[0.0005180779,0.0001604642,0.0005801167,0.000007551568,0.000005875551,0.00001404922,0.00001718056,0.9904796,0.0001144918,0.007852856,0.0001263467,0.0001233713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004284299,0.0003637559,0.9502754,0.0001886906,0.0001533334,0.0001726675,8.515587e-7,0.0001438248,0.0444172],"genre_scores_gemma":[0.967161,0.00002167903,0.0292535,0.0001047977,0.00006645921,0.00002377432,3.306848e-7,0.000005902787,0.003362538],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9628767,"threshold_uncertainty_score":0.4049459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02993687719976868,"score_gpt":0.2118822715717385,"score_spread":0.1819453943719699,"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."}}