{"id":"W2123716062","doi":"10.1109/cca.2005.1507105","title":"A systematic method of adaptive fuzzy logic modeling, using an improved fuzzy c-means clustering algorithm for rule generation","year":2005,"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":"Queen's University","funders":"","keywords":"Fuzzy logic; Computer science; Defuzzification; Fuzzy number; Gradient descent; Fuzzy classification; Cluster analysis; Heuristic; Algorithm; Fuzzy associative matrix; Data mining; Fuzzy set operations; Benchmark (surveying); Neuro-fuzzy; Mathematical optimization; Artificial intelligence; Fuzzy control system; Fuzzy set; Mathematics; Artificial neural network","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":[0.001342044,0.0002395337,0.0005932038,0.0001312403,0.0001655781,0.0001440723,0.0006093829,0.0001270607,0.000001282618],"category_scores_gemma":[0.00003723252,0.0001881945,0.0001715951,0.00019259,0.00001566748,0.0008011098,0.0001261504,0.00007731135,0.000003763207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001285077,"about_ca_system_score_gemma":0.00008721167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003063907,"about_ca_topic_score_gemma":0.0001025816,"domain_scores_codex":[0.9977475,0.0002751329,0.0007983696,0.0005374175,0.0002728143,0.0003687739],"domain_scores_gemma":[0.9985697,0.00009189158,0.0003229772,0.0005841669,0.0003190291,0.0001122882],"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":[0.00002075442,0.0002430752,0.000001113624,0.001350343,0.0001804257,0.000002301274,0.001940674,0.8107174,0.0462905,0.05587918,0.00001477865,0.08335947],"study_design_scores_gemma":[0.0006461953,0.0002403387,2.964493e-7,0.0001708123,0.00004579929,0.00003102087,0.0003180634,0.9924039,0.0005798167,0.005329403,0.00000135842,0.000233004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005684513,0.0002768125,0.995902,0.00006757772,0.0002754342,0.001291451,0.000006832319,0.0001469519,0.001464477],"genre_scores_gemma":[0.2356821,0.000001973839,0.7636196,0.0001478246,0.0003032043,0.0001182128,0.000003233785,0.000014121,0.000109743],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2351136,"threshold_uncertainty_score":0.7674347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06450352089494382,"score_gpt":0.296535817907541,"score_spread":0.2320322970125971,"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."}}