{"id":"W2624726413","doi":"10.1049/el.2017.1812","title":"Bias‐compensated robust set‐membership NLMS algorithm against impulsive noises and noisy inputs","year":2017,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Sichuan Province Youth Science and Technology Innovation Team","keywords":"Algorithm; Set (abstract data type); Computer science; Noise (video); Control theory (sociology); Robustness (evolution); Mathematics; Artificial intelligence","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":[0.0008078634,0.0006571572,0.0008564839,0.000528818,0.0004105306,0.0007899191,0.001465386,0.001177023,0.001357994],"category_scores_gemma":[0.002885224,0.0003134314,0.0005234436,0.0005015979,0.0006619435,0.0009567541,0.000833357,0.001028516,0.00061375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006060511,"about_ca_system_score_gemma":0.0008695692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00212487,"about_ca_topic_score_gemma":0.001825449,"domain_scores_codex":[0.9994627,0.000105656,0.00003210894,0.0001199715,0.0002415356,0.00003819254],"domain_scores_gemma":[0.9992004,0.0003353059,0.0001207522,0.00008894574,0.0002336707,0.00002085106],"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":[0.0002407979,0.00005385541,0.0005136124,0.0001822325,0.00008101851,0.00007149559,0.0002236666,0.5680265,0.03444359,0.01192288,0.001294314,0.3829461],"study_design_scores_gemma":[0.000008466906,0.00003160587,0.0001437454,0.000009030741,0.000006672481,0.00003020269,0.000009330488,0.9896718,0.007168368,0.001984573,0.0009239043,0.00001229957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003713193,0.00006570898,0.9954545,0.00003228366,0.00001687277,0.00001088015,0.000006796788,0.0001798884,0.0005198321],"genre_scores_gemma":[0.3395456,0.0001774284,0.6546777,0.0001116156,0.00005913471,0.0001725427,0.0001080544,0.0001213021,0.005026495],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00212487,"threshold_uncertainty_score":0.004542947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0280158838767748,"score_gpt":0.2406760514932215,"score_spread":0.2126601676164467,"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."}}