{"id":"W4391288516","doi":"10.1016/j.knosys.2024.111398","title":"Adaptive Nonstationary Fuzzy Neural Network","year":2024,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National University's Basic Research Foundation of China; National Natural Science Foundation of China","keywords":"Adaptive neuro fuzzy inference system; Generalization; Artificial neural network; Computer science; Neuro-fuzzy; Interval (graph theory); Data mining; Artificial intelligence; Fuzzy logic; Fuzzy rule; Inference; Cluster analysis; Fuzzy set; Fuzzy control system; 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007994768,0.0003063955,0.0003883614,0.0001867578,0.0002374869,0.0006158609,0.0008734121,0.000141009,0.000005758702],"category_scores_gemma":[0.00002377639,0.0002519013,0.0002092164,0.001039405,0.000067516,0.0004385928,0.000114128,0.0002556081,0.001367377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001728559,"about_ca_system_score_gemma":0.0004537651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008029374,"about_ca_topic_score_gemma":0.00002309092,"domain_scores_codex":[0.9973167,0.0004716299,0.0005239221,0.000724757,0.0003843201,0.0005786559],"domain_scores_gemma":[0.9981971,0.000645332,0.00009317772,0.0006633373,0.0002016149,0.0001994371],"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":[0.00003546298,0.0001160592,0.0004115439,0.0004305749,0.0001457072,0.0003327679,0.000850816,0.06025929,0.0001276987,0.8254848,0.08657824,0.02522705],"study_design_scores_gemma":[0.0004089281,0.0001867869,0.0002293783,0.0004291387,0.00001839642,0.00005753399,0.00008957495,0.9587649,0.000008196014,0.003753273,0.03568489,0.0003690556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009200246,0.07095166,0.6154337,0.0009501706,0.02208048,0.001306828,0.00002776347,0.002322934,0.2860064],"genre_scores_gemma":[0.9938627,0.00000420893,0.001075598,0.0001225675,0.001875302,0.0002571683,0.00001147309,0.00002935948,0.002761568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9929428,"threshold_uncertainty_score":0.9999933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02107891936946964,"score_gpt":0.2423304168899798,"score_spread":0.2212514975205101,"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."}}