{"id":"W1834156195","doi":"10.1109/ijcnn.1992.227334","title":"A comparison between Kalman filters and recurrent neural networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Kalman filter; Extended Kalman filter; Artificial neural network; Computer science; Estimator; Fast Kalman filter; Invariant extended Kalman filter; Alpha beta filter; Artificial intelligence; SIGNAL (programming language); Control theory (sociology); Mathematics; Moving horizon estimation; Statistics","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":[0.003592976,0.0005827287,0.0008712544,0.0008009925,0.0002970021,0.001165724,0.0006698467,0.001028531,0.00196468],"category_scores_gemma":[0.01554539,0.0002954561,0.0004504171,0.0008493554,0.000353046,0.001984513,0.0004383969,0.0005080691,0.0003854735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009227312,"about_ca_system_score_gemma":0.000776155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01063728,"about_ca_topic_score_gemma":0.007455921,"domain_scores_codex":[0.9986396,0.0004762144,0.00008438309,0.0002215781,0.0004762961,0.0001018666],"domain_scores_gemma":[0.992179,0.005788874,0.000401464,0.0004576248,0.001104973,0.00006811404],"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":[0.000828705,0.00007070484,0.00356786,0.0002696893,0.0003279063,0.00007000009,0.0001297496,0.7162759,0.003843224,0.02427928,0.001132636,0.2492044],"study_design_scores_gemma":[0.00002419063,0.0001406752,0.001209717,0.00002869382,0.00005706186,0.00002433458,0.00002033709,0.9909509,0.002640657,0.003721935,0.001159313,0.00002205239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09109967,0.003315272,0.8959998,0.0003467634,0.0002254183,0.00004271888,0.0001587356,0.00186645,0.006945073],"genre_scores_gemma":[0.847809,0.001905855,0.1461782,0.00009321016,0.0001113461,0.00005383361,0.0002471559,0.0001767073,0.003424605],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01063728,"threshold_uncertainty_score":0.02115071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03804759846307454,"score_gpt":0.2969830682251434,"score_spread":0.2589354697620689,"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."}}