{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001618114,0.0003424335,0.0003956636,0.0007041583,0.0002030934,0.0004739388,0.0005597355,0.0005458185,0.001255022],"category_scores_gemma":[0.008866484,0.0002443271,0.0004524056,0.0004692003,0.0010857,0.001140223,0.0005233058,0.000875416,0.000150874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007660277,"about_ca_system_score_gemma":0.0007483313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002395037,"about_ca_topic_score_gemma":0.001718793,"domain_scores_codex":[0.9994835,0.0001541283,0.00002325831,0.00007374635,0.0002215519,0.00004395774],"domain_scores_gemma":[0.9961463,0.002696922,0.0003682914,0.0001840027,0.0005575872,0.00004700205],"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":[0.00005089869,0.00002660766,0.001750792,0.0001132073,0.00006392557,0.0001315876,0.0001047568,0.8336116,0.005966955,0.1282217,0.0005137335,0.02944422],"study_design_scores_gemma":[0.000001098682,0.000007962818,0.00055908,0.000003713681,0.000003253545,0.00001900969,0.000004311234,0.9814197,0.0005010715,0.01731363,0.0001619713,0.000005342054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03650445,0.0005332574,0.9600005,0.0002163497,0.00002596908,0.0000175354,0.00003790089,0.0001232747,0.002540709],"genre_scores_gemma":[0.9432015,0.0008106643,0.05160376,0.0001032358,0.0001061611,0.00008524577,0.0001523173,0.0001117457,0.003825421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002395037,"threshold_uncertainty_score":0.008557498,"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."}}