{"id":"W2161892539","doi":"10.1109/ijcnn.1992.226870","title":"On the application of feed forward neural networks to channel equalization","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Quadrature amplitude modulation; QAM; Artificial neural network; Channel (broadcasting); Computer science; Equalization (audio); Feedforward neural network; Blind equalization; Binary number; Feed forward; Electronic engineering; Algorithm; Telecommunications; Artificial intelligence; Mathematics; Engineering; Bit error rate; Arithmetic","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":[0.0008039859,0.0005361657,0.0003972998,0.0004622025,0.0002686308,0.0008493463,0.0004504443,0.001062431,0.002239738],"category_scores_gemma":[0.004297845,0.000265041,0.0002857532,0.0005963705,0.0009925356,0.00116387,0.000634056,0.001048414,0.0003977766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005271577,"about_ca_system_score_gemma":0.0002704247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004378242,"about_ca_topic_score_gemma":0.003710884,"domain_scores_codex":[0.9996567,0.0001457788,0.00001567959,0.00003578168,0.0001174242,0.00002863086],"domain_scores_gemma":[0.9988605,0.0008869084,0.00002985932,0.00004248574,0.0001695645,0.00001065634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001134487,0.00003383226,0.000403328,0.0001323068,0.00006997136,0.0001965851,0.00007449599,0.7487971,0.003094099,0.08818518,0.002106629,0.1567931],"study_design_scores_gemma":[0.000006375241,0.00002832972,0.0001297892,0.00002606165,0.000009030549,0.00003939434,0.000009191403,0.9544309,0.001399007,0.0420055,0.001907367,0.000008986588],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01416836,0.005916371,0.9624251,0.001227596,0.0002904299,0.00002249967,0.00003041556,0.0002563211,0.01566293],"genre_scores_gemma":[0.7434123,0.01708113,0.2071499,0.0009510576,0.0008881172,0.00011032,0.0001067402,0.0001020524,0.03019846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004378242,"threshold_uncertainty_score":0.008705497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0198533154031359,"score_gpt":0.2553413895625704,"score_spread":0.2354880741594345,"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."}}