{"id":"W1542153889","doi":"","title":"Statistical analysis of adaptive neural network inversion of Hammerstein systems for Gaussian inputs","year":2002,"lang":"en","type":"article","venue":"European Signal Processing Conference","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Artificial neural network; Multilayer perceptron; Adaptive filter; Kernel adaptive filter; Computer science; Backpropagation; Adaptive system; Gaussian; Perceptron; Algorithm; Nonlinear system; Wiener filter; Filter (signal processing); Least mean squares filter; Activation function; Control theory (sociology); Deconvolution; Inversion (geology); Mathematics; Artificial intelligence; Filter design","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":[0.000270136,0.0001410807,0.0003280436,0.0001194045,0.0001368575,0.0001049702,0.0006113686,0.00002702951,0.00002019651],"category_scores_gemma":[0.00001303466,0.0001240399,0.00008498063,0.001027284,0.0001290888,0.0002188221,0.000127992,0.0001064722,0.000004756745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001188095,"about_ca_system_score_gemma":0.00002418091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001234252,"about_ca_topic_score_gemma":0.000002361193,"domain_scores_codex":[0.9985337,0.0001760708,0.0004364863,0.0003646629,0.0002379525,0.0002511772],"domain_scores_gemma":[0.9988453,0.0001753564,0.0003840971,0.0002614564,0.0002334187,0.0001003938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001254458,0.0003673205,0.002638746,0.0004886939,0.0006680394,0.00003893114,0.002692825,0.3486159,0.007265226,0.1942345,0.009479628,0.4333848],"study_design_scores_gemma":[0.0001650948,0.000183573,0.001047471,0.00009611376,0.0001250888,0.000001297343,0.00002427989,0.9975256,0.000159117,0.0001940403,0.0003460918,0.0001321786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005684447,0.0003289553,0.9910545,0.0001820774,0.00004628023,0.0001910982,0.00002244213,0.00004488312,0.002445363],"genre_scores_gemma":[0.9886237,0.000009883867,0.01110512,0.00006409031,0.00006492685,0.000007135991,0.00001063238,0.00001048259,0.0001039649],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9829393,"threshold_uncertainty_score":0.50582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05691095865527767,"score_gpt":0.2564117848771848,"score_spread":0.1995008262219071,"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."}}