{"id":"W2127255779","doi":"10.1109/isspa.2007.4555423","title":"Neural network modeling and identification of nonlinear MIMO channels","year":2007,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Utah Agricultural Experiment Station","keywords":"MIMO; Nonlinear system; Artificial neural network; Computer science; Control theory (sociology); Perceptron; Convergence (economics); Algorithm; Mean squared error; Multilayer perceptron; Mathematics; Channel (broadcasting); Artificial intelligence; 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":[0.000346006,0.0004410679,0.0004463371,0.000272685,0.0002024288,0.0004297448,0.0005520381,0.0007244411,0.0005677934],"category_scores_gemma":[0.001242875,0.0002576952,0.0002859574,0.0003038314,0.0003696228,0.0006826353,0.0003390219,0.0006627815,0.000163521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004162462,"about_ca_system_score_gemma":0.0004054178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004686696,"about_ca_topic_score_gemma":0.003098183,"domain_scores_codex":[0.9998247,0.00005283548,0.000008569615,0.00004129306,0.00005123873,0.00002136488],"domain_scores_gemma":[0.9996976,0.0001623819,0.00004599351,0.00001913077,0.00006659763,0.000008280336],"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":[0.00001579928,0.000006667015,0.000221006,0.0000184098,0.00001090331,0.00002773166,0.00001213485,0.9877988,0.001524136,0.002407782,0.00008521124,0.007871326],"study_design_scores_gemma":[5.607897e-7,0.000001923991,0.00004398051,7.88347e-7,9.828217e-7,0.000003939484,8.696589e-7,0.9990822,0.0001990439,0.0006162123,0.00004823046,0.000001262448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02809164,0.0003693653,0.9692202,0.0001169874,0.00003196709,0.00001449168,0.00005135906,0.0001886545,0.001915316],"genre_scores_gemma":[0.9270285,0.0006479782,0.06787688,0.00005187428,0.00005285591,0.0001092152,0.0001024746,0.00002797767,0.004102216],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004686696,"threshold_uncertainty_score":0.009318829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0258845227527745,"score_gpt":0.2858807831462967,"score_spread":0.2599962603935222,"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."}}