{"id":"W1567364005","doi":"10.1007/978-3-540-75396-4_2","title":"Genetically Optimized Hybrid Fuzzy Neural Networks: Analysis and Design of Rule-based Multi-layer Perceptron Architectures","year":2008,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Perceptron; Neuro-fuzzy; Adaptive neuro fuzzy inference system; Genetic algorithm; Fuzzy logic; Fuzzy rule; Computational intelligence; Machine learning; Fuzzy control system","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"],"consensus_categories":[],"category_scores_codex":[0.0004059957,0.0005000086,0.001148782,0.0005755054,0.0001483793,0.00004887619,0.0008019081,0.0001406103,0.000007852581],"category_scores_gemma":[0.00009905233,0.0004352281,0.0003041307,0.0002597657,0.0007810869,0.00005284241,0.0003689986,0.0003817176,0.000004947373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009448767,"about_ca_system_score_gemma":0.0001109003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002770548,"about_ca_topic_score_gemma":0.00001359553,"domain_scores_codex":[0.997004,0.0002002095,0.0009875932,0.000868349,0.0005921049,0.0003476991],"domain_scores_gemma":[0.9966853,0.001929621,0.0004167716,0.0004134258,0.0004597672,0.00009515705],"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":[0.00006916475,0.00003547645,0.0000698974,0.00003946143,0.0006866384,0.00006692202,0.0003860826,0.9764685,0.000001115791,0.005598663,0.00005616498,0.0165219],"study_design_scores_gemma":[0.0003099039,0.0001561963,0.0005474188,0.0001046005,0.0001401063,0.00002642446,0.00002232139,0.9731113,0.00000504222,0.02517415,0.00001408087,0.000388422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001241583,0.01282246,0.9842499,0.0001345787,0.0002672632,0.000522511,0.00001454089,0.00005588608,0.001808627],"genre_scores_gemma":[0.6574156,0.001162099,0.3400877,0.0002639701,0.0001033139,0.00005248644,0.00001756021,0.00002599442,0.0008712272],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6572915,"threshold_uncertainty_score":0.99981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08336795716151553,"score_gpt":0.3084911420645933,"score_spread":0.2251231849030778,"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."}}