{"id":"W2066981453","doi":"10.1002/cjce.5450780211","title":"Neural network‐based optimal iterative controller for nonlinear processes","year":2000,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Control theory (sociology); Artificial neural network; Nonlinear system; Computer science; Controller (irrigation); Convergence (economics); Iterative learning control; PID controller; Computation; Optimal control; Process (computing); Tracking error; Iterative method; Control engineering; Mathematics; Mathematical optimization; Algorithm; Control (management); Artificial intelligence; Engineering; Temperature control","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006163084,0.0005534242,0.0004986262,0.0003389638,0.0003186475,0.0005631872,0.0008781123,0.0005710283,0.001072687],"category_scores_gemma":[0.001121443,0.0002045535,0.0002672883,0.0003085613,0.0004439149,0.0003653601,0.0005455535,0.0006167078,0.0001996374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007743631,"about_ca_system_score_gemma":0.0007977768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007001899,"about_ca_topic_score_gemma":0.005739779,"domain_scores_codex":[0.9996448,0.0000568969,0.00002078525,0.00006092329,0.0001744778,0.00004226719],"domain_scores_gemma":[0.9997492,0.00007492703,0.0000396213,0.00001425006,0.0001113466,0.00001065583],"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":[0.0001159872,0.00007841281,0.0003118149,0.0001125762,0.00004067611,0.00008489798,0.00007804344,0.8727617,0.01375945,0.009950288,0.001308691,0.1013974],"study_design_scores_gemma":[0.00000912069,0.0000190662,0.0000491244,0.000002712923,0.00000322202,0.000007077715,0.000001304251,0.9983137,0.0007817632,0.0004364391,0.0003734155,0.000003095309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01917748,0.0004973959,0.9726466,0.0001057335,0.00008151463,0.00005762343,0.00001664779,0.0003629289,0.007053962],"genre_scores_gemma":[0.8979073,0.0002690837,0.09677068,0.0001023047,0.00004411035,0.0002203304,0.00005869583,0.00003075079,0.004596765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007001899,"threshold_uncertainty_score":0.01392227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005504127800919893,"score_gpt":0.1865331940389669,"score_spread":0.181029066238047,"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."}}