{"id":"W2523594788","doi":"10.1109/tfuzz.2016.2612267","title":"Fuzzy Wavelet Polynomial Neural Networks: Analysis and Design","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; National Natural Science Foundation of China","keywords":"Wavelet; Artificial neural network; Polynomial; Computer science; Curse of dimensionality; Polynomial regression; Fuzzy logic; Artificial intelligence; Mathematics; Mathematical optimization; Algorithm; Machine learning; Regression analysis","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":[0.0006388475,0.0003452905,0.0006108152,0.0004575041,0.0003388919,0.0003367942,0.0006640885,0.0001976457,0.000004287632],"category_scores_gemma":[0.000006294812,0.0002316207,0.0002840364,0.001046021,0.00009318761,0.000541845,0.000005688718,0.0001805715,0.0000639647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001158353,"about_ca_system_score_gemma":0.00004729082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003860976,"about_ca_topic_score_gemma":0.00005620357,"domain_scores_codex":[0.9971091,0.0005384519,0.0005695542,0.0007727211,0.0004262468,0.0005839771],"domain_scores_gemma":[0.9980257,0.0005267918,0.0001822679,0.0008753202,0.00009760283,0.0002922931],"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":[0.0006417388,0.0006426499,0.0008951359,0.0001263491,0.004866405,0.0002325577,0.001124179,0.5174397,0.00637076,0.01185699,0.006236527,0.449567],"study_design_scores_gemma":[0.004711372,0.001173379,0.001707449,0.0001896592,0.0009097506,0.0002981805,0.0001868991,0.9862569,0.000848903,0.0008154607,0.001166367,0.00173571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002282826,0.0004028241,0.9915097,0.0006004403,0.002531914,0.0005464537,0.00001982187,0.0003378912,0.00176814],"genre_scores_gemma":[0.9972452,0.00005414227,0.000875477,0.0001313869,0.0002830396,0.0001656913,5.887e-7,0.00002103084,0.001223449],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9949624,"threshold_uncertainty_score":0.9445217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01737011780069887,"score_gpt":0.2129747508540395,"score_spread":0.1956046330533406,"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."}}