{"id":"W2102634164","doi":"10.1109/adfsp.1998.685690","title":"Digital filter design using genetic algorithm","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Minimax; Finite impulse response; Algorithm; Computer science; Rate of convergence; Coding (social sciences); Digital filter; Convergence (economics); Filter design; Genetic algorithm; Filter (signal processing); Adaptive filter; Mathematical optimization; Mathematics; Statistics; 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":[0.0005495643,0.0005558687,0.0006453666,0.0008038821,0.0003466945,0.0006803994,0.0006286911,0.001152534,0.001984927],"category_scores_gemma":[0.001316572,0.0003122679,0.0005697626,0.0007482458,0.0004796062,0.0004561546,0.000363286,0.0005697396,0.0005599809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005972969,"about_ca_system_score_gemma":0.0006649031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002796924,"about_ca_topic_score_gemma":0.002598851,"domain_scores_codex":[0.9997352,0.00007751641,0.00001590399,0.00005523325,0.00008378238,0.000032479],"domain_scores_gemma":[0.999711,0.0001506497,0.00002785028,0.00001883626,0.00008290415,0.000008633523],"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":[0.00004788472,0.00003956977,0.0004045419,0.00008472239,0.00006494491,0.00006097845,0.00007569743,0.7548227,0.008026888,0.01991942,0.001390502,0.2150621],"study_design_scores_gemma":[0.0000263634,0.00004565381,0.0001113657,0.00001850748,0.00002065537,0.00004912482,0.00001027578,0.9845804,0.002981449,0.008109543,0.004036188,0.00001044974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005445096,0.000216179,0.9914518,0.00007021144,0.00003393961,0.00003265409,0.00001615801,0.0003644977,0.002369494],"genre_scores_gemma":[0.1527232,0.0004585656,0.8423374,0.0001368937,0.00002910551,0.0002370875,0.0000843165,0.00005308614,0.003940248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002796924,"threshold_uncertainty_score":0.006640255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0467766655788698,"score_gpt":0.2255719011014042,"score_spread":0.1787952355225344,"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."}}