{"id":"W1970796842","doi":"10.1109/med.2014.6961416","title":"Design of Fuzzy Terminal Iterative Learning Control based on internal model control","year":2014,"lang":"en","type":"article","venue":"","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Fuzzy control system; Control theory (sociology); Iterative learning control; Fuzzy logic; Computer science; Defuzzification; Neuro-fuzzy; Controller (irrigation); Interpolation (computer graphics); Adaptive neuro fuzzy inference system; Process (computing); Terminal (telecommunication); Control engineering; Mathematical optimization; Fuzzy number; Control (management); Mathematics; Artificial intelligence; Fuzzy set; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006721859,0.0002837745,0.0005084139,0.0001885789,0.00006379576,0.0000675924,0.000186907,0.0001020401,0.00005995932],"category_scores_gemma":[0.0002502859,0.0002429848,0.0001082784,0.00007872702,0.00004381041,0.0001308024,0.000007124248,0.0004184799,0.00005341308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007474583,"about_ca_system_score_gemma":0.00002249975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008452574,"about_ca_topic_score_gemma":0.000001340205,"domain_scores_codex":[0.9982224,0.0004999162,0.000433862,0.0002360397,0.0002807103,0.0003271034],"domain_scores_gemma":[0.9984655,0.0009888168,0.0001162578,0.0002127791,0.0001298613,0.00008678818],"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":[0.0002416947,0.0000222436,0.001071212,0.00003489073,0.00005779028,0.00000404724,0.0003198987,0.9715815,0.0248654,0.0005643089,0.0001404537,0.001096592],"study_design_scores_gemma":[0.004423538,0.0005445775,0.0002442946,0.0001131634,0.00002806646,0.000003817149,0.00001864013,0.9928947,0.001298786,0.00005037164,0.0001437077,0.0002363519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00819461,0.0000203638,0.9752427,0.00005505717,0.0001462526,0.0003927482,0.000005228318,0.0002679338,0.01567507],"genre_scores_gemma":[0.9961201,2.438273e-7,0.002546295,0.0002704595,0.0001321932,0.00005975162,0.000003013299,0.00005429585,0.0008136515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9879255,"threshold_uncertainty_score":0.9908633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007965282117141115,"score_gpt":0.208389637407567,"score_spread":0.2004243552904259,"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."}}