{"id":"W2167522158","doi":"10.1109/iecon.2005.1569042","title":"A new analysis and design method for fuzzy logic controllers used in power converter","year":2005,"lang":"en","type":"article","venue":"","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Fuzzy logic; Converters; Control theory (sociology); Control engineering; Computer science; Fuzzy electronics; Fuzzy control system; Controller (irrigation); Heuristic; Small-signal model; Power (physics); Engineering; Neuro-fuzzy; Control (management); Voltage; Artificial intelligence","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":[0.0008483075,0.0001463436,0.0004617703,0.0002419558,0.0000414689,0.0001360474,0.0003627809,0.00008281595,0.00002782341],"category_scores_gemma":[0.0000368012,0.0001054364,0.0001450555,0.0005108106,0.00001372237,0.0002597663,0.00004974558,0.00005527016,0.00001617136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003736054,"about_ca_system_score_gemma":0.000063477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003121764,"about_ca_topic_score_gemma":0.0002455176,"domain_scores_codex":[0.9986608,0.0001753991,0.0002950509,0.0004210565,0.0001474729,0.0003002149],"domain_scores_gemma":[0.9989465,0.0005024754,0.00007190986,0.0003084675,0.0000414993,0.0001290858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000372051,0.0001705145,0.00664651,0.00001681642,0.001433068,0.00002414957,0.002893739,0.009521531,0.002740073,0.7153652,0.007805652,0.2530107],"study_design_scores_gemma":[0.005608957,0.0002137095,0.003211033,0.000004146761,0.0001104779,0.000006200344,0.00009126672,0.9594841,0.0001316913,0.02920718,0.001633271,0.00029794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001270826,0.0002466367,0.988658,0.003441004,0.0000633829,0.0005289794,7.169133e-7,0.00006959369,0.006864578],"genre_scores_gemma":[0.5550062,0.000002418171,0.4420011,0.001664064,0.00002799307,0.00003913328,3.702681e-7,0.000003438171,0.001255318],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9499626,"threshold_uncertainty_score":0.4299572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0256201635158489,"score_gpt":0.2707789985318777,"score_spread":0.2451588350160288,"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."}}