{"id":"W2745650441","doi":"10.1002/asjc.1635","title":"An E‐HOIM Based Data‐Driven Adaptive TILC of Nonlinear Discrete‐Time Systems for Non‐Repetitive Terminal Point Tracking","year":2017,"lang":"en","type":"article","venue":"Asian Journal of Control","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Control theory (sociology); Iterative learning control; Nonlinear system; Internal model; Linearization; Terminal (telecommunication); Computer science; Convergence (economics); Adaptive control; Linear system; Mathematics; Control (management); 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001213989,0.0002938488,0.0009721317,0.0001938553,0.0001902922,0.0002905182,0.001123708,0.000123597,0.00001709171],"category_scores_gemma":[0.0003785302,0.0002540718,0.0002382004,0.00004043278,0.0001096273,0.001255513,0.00002437165,0.0003837345,0.000008485422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008781104,"about_ca_system_score_gemma":0.0001318656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001417417,"about_ca_topic_score_gemma":0.00000950612,"domain_scores_codex":[0.9978562,0.0002180541,0.0009406307,0.0002425534,0.0003735393,0.0003690457],"domain_scores_gemma":[0.9968355,0.0002588885,0.001216461,0.0008910378,0.000599167,0.0001989957],"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":[0.008847634,0.0008048527,0.02224291,0.00183946,0.007555665,0.001850701,0.01074874,0.6921895,0.1992544,0.0008579405,0.0024567,0.05135153],"study_design_scores_gemma":[0.00585771,0.001209907,0.006174293,0.0009953214,0.0002515092,0.0001214798,0.0004770202,0.9833701,0.0005103821,0.00001166448,0.0007452152,0.0002753564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06634708,0.0005195466,0.9253511,0.0006119533,0.001682089,0.001846201,0.001318458,0.0000816458,0.002241934],"genre_scores_gemma":[0.9956133,0.000001955461,0.00297835,0.00001869861,0.001226717,0.00001686645,0.00002344493,0.00007203797,0.00004866813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9292662,"threshold_uncertainty_score":0.9999912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01506400673013887,"score_gpt":0.2696035754857114,"score_spread":0.2545395687555725,"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."}}