{"id":"W4417052040","doi":"10.23919/eusipco63237.2025.11226097","title":"A Practical Regularized Recursive Least-Squares Algorithm for Robust System Identification","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Robustness (evolution); System identification; Regularization (linguistics); Adaptive filter; Convergence (economics); Recursive least squares filter; Linear system; Adaptive algorithm","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":[0.0008114641,0.0007623299,0.0008136724,0.0003590312,0.0002786743,0.0005533356,0.0007750932,0.001053145,0.002839635],"category_scores_gemma":[0.002103994,0.0003467377,0.0007321726,0.000486082,0.0004905924,0.0006958728,0.0006636518,0.001210652,0.001853299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002867923,"about_ca_system_score_gemma":0.0009495703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001247907,"about_ca_topic_score_gemma":0.001432406,"domain_scores_codex":[0.9993507,0.0001937909,0.00003430383,0.0001567269,0.0002352375,0.00002921355],"domain_scores_gemma":[0.9995803,0.0001469928,0.00004664021,0.00008196149,0.0001304697,0.00001370261],"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":[0.0001044305,0.00006444238,0.0003130051,0.0002209108,0.00009906598,0.0001537428,0.0001177615,0.5360069,0.03227971,0.0466953,0.004334585,0.3796102],"study_design_scores_gemma":[0.000009417424,0.00003899827,0.00006517168,0.000008150202,0.00000629761,0.00007465218,0.000005130727,0.987927,0.002478276,0.003988454,0.005386469,0.00001195546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005890136,0.00005683817,0.9987702,0.00003261505,0.00001667343,0.0000118544,0.000009708698,0.0001876486,0.0003253551],"genre_scores_gemma":[0.06037437,0.0001968131,0.9363609,0.00007426767,0.00005058238,0.0001273995,0.0001430235,0.00010706,0.002565602],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002839635,"threshold_uncertainty_score":0.00949949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03048361577951771,"score_gpt":0.3100367612838152,"score_spread":0.2795531455042975,"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."}}