{"id":"W2109734802","doi":"10.1109/iscas.2000.856333","title":"Complex EKF neural network for adaptive equalization","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial neural network; Equalization (audio); Adaptive equalizer; Channel (broadcasting); Convergence (economics); Backpropagation; Extended Kalman filter; Adaptive filter; Blind equalization; Kalman filter; Adaptive system; Control theory (sociology); Artificial intelligence; Algorithm; Telecommunications","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":[0.0003925468,0.0003643828,0.000390348,0.0002759118,0.0001991368,0.0005246534,0.0003927794,0.000808055,0.002591566],"category_scores_gemma":[0.001599843,0.0001205203,0.0001898095,0.0003426181,0.0002530565,0.0007188439,0.0002415,0.0007391202,0.0005624496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004780104,"about_ca_system_score_gemma":0.0005243489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003935278,"about_ca_topic_score_gemma":0.003193576,"domain_scores_codex":[0.9998054,0.00004555389,0.00001243475,0.00004102867,0.00007894378,0.00001669418],"domain_scores_gemma":[0.9997347,0.00009869391,0.00001790611,0.00002462957,0.0001195187,0.000004515463],"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":[0.0001957012,0.00004959393,0.0008808429,0.0002255309,0.00005587089,0.0001233738,0.00004792518,0.4896392,0.01694752,0.04280466,0.00648127,0.4425485],"study_design_scores_gemma":[0.00001039259,0.00001364192,0.0001985428,0.000009046137,0.000007113184,0.00004879418,0.000002860746,0.9896578,0.002281781,0.004045882,0.003715388,0.000008823557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004892184,0.00124868,0.990002,0.0002696147,0.0002074813,0.00002727352,0.00005088512,0.0003516431,0.002950242],"genre_scores_gemma":[0.5637063,0.002671647,0.4127113,0.0003458019,0.0002333489,0.0002332294,0.0002752258,0.000082909,0.01974024],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003935278,"threshold_uncertainty_score":0.008669674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1081253122161967,"score_gpt":0.2839620602074152,"score_spread":0.1758367479912184,"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."}}