{"id":"W2051435691","doi":"10.1016/j.mcm.2004.05.006","title":"Nonlinear channel blind equalization using hybrid genetic algorithm with simulated annealing","year":2005,"lang":"en","type":"article","venue":"Mathematical and Computer Modelling","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Simulated annealing; Algorithm; Nonlinear system; Blind equalization; Computer science; Genetic algorithm; Channel (broadcasting); Gaussian; Fitness function; Binary number; Mathematical optimization; Equalization (audio); Mathematics; Decoding methods; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002627004,0.0002042457,0.000245116,0.0001307984,0.0001585566,0.000331385,0.0002743809,0.00006373887,0.000003824622],"category_scores_gemma":[0.000002517386,0.0001692297,0.00003904928,0.0002211535,0.00003962269,0.0004394335,0.0001589609,0.0001408319,0.0000092723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000217222,"about_ca_system_score_gemma":0.00002899949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004932179,"about_ca_topic_score_gemma":2.2141e-7,"domain_scores_codex":[0.9986054,0.00006106455,0.0003735126,0.0004094184,0.0002826318,0.0002679312],"domain_scores_gemma":[0.9992727,0.00008347142,0.000106655,0.0002960228,0.0001229278,0.0001181894],"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":[0.000004573405,0.00008036353,0.000001104669,0.00002704844,0.00001495462,0.000007409751,0.001043382,0.9709579,0.00001715145,0.008800977,0.000004140787,0.019041],"study_design_scores_gemma":[0.0003642076,0.00008259552,5.240367e-7,0.0000968278,0.00001216375,0.00008581815,0.000006182608,0.9816331,0.0012602,0.01614779,0.00007492643,0.0002357118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06802575,0.00007467264,0.9311392,0.0001600726,0.00002241402,0.0002142305,0.000001557713,0.0003200674,0.00004206744],"genre_scores_gemma":[0.1855992,0.00001062155,0.8138632,0.0003446011,0.0001418789,0.000003188565,0.000004304803,0.00001869225,0.00001431947],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1175735,"threshold_uncertainty_score":0.6900985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03946262079563609,"score_gpt":0.2631409369657214,"score_spread":0.2236783161700854,"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."}}