{"id":"W2946350616","doi":"10.1109/tnnls.2019.2911603","title":"Convolutional Neural Networks as Asymmetric Volterra Models Based on Generalized Orthonormal Basis Functions","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Control Systems and Identification","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Orthonormal basis; Convolutional neural network; Noise (video); Computer science; Nonlinear system; System identification; Quadcopter; Nonlinear system identification; Basis (linear algebra); Applied mathematics; Artificial neural network; Basis function; Dynamical systems theory; Volterra series; Identification (biology); Orthogonal functions; Algorithm; Artificial intelligence; Mathematics; Data modeling; Mathematical analysis; Engineering; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002726228,0.0005513983,0.0004033437,0.0003875337,0.0001813757,0.0005882305,0.000640012,0.0006027725,0.001287167],"category_scores_gemma":[0.0007312559,0.0003025693,0.0005799704,0.0005032526,0.0003181724,0.0008092814,0.0003645252,0.0008553255,0.0004720218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007088803,"about_ca_system_score_gemma":0.0004310143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00535295,"about_ca_topic_score_gemma":0.006520429,"domain_scores_codex":[0.9998354,0.00003988571,0.000009726302,0.00003134519,0.00006266023,0.00002094645],"domain_scores_gemma":[0.9998816,0.00003761676,0.00001895657,0.00002036055,0.0000345038,0.000007000053],"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":[0.00002664836,0.00002122678,0.0004399527,0.000087005,0.00004845898,0.00009962747,0.00004481996,0.8482315,0.01223429,0.08916813,0.001492426,0.04810587],"study_design_scores_gemma":[9.519274e-7,0.000004868041,0.00009011388,0.000007094904,0.000004126192,0.00001971931,0.000002849752,0.9894433,0.0009185902,0.008282393,0.001221909,0.000004168886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01449861,0.0005731481,0.9795952,0.000135274,0.00008926258,0.00002582215,0.000163448,0.0003533111,0.004565896],"genre_scores_gemma":[0.7030649,0.001930067,0.2810889,0.000173617,0.00009706688,0.0001496489,0.0006471469,0.0002033058,0.01264539],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00535295,"threshold_uncertainty_score":0.0106436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009914220327719762,"score_gpt":0.1922569441142461,"score_spread":0.1823427237865263,"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."}}