{"id":"W2966956762","doi":"10.1109/cec.2019.8789894","title":"Enhancing LQR Controller Using Optimized Real-time System by GDE3 and NSGA-II Algorithms and Comparing with Conventional Method","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Sorting; Reliability (semiconductor); Computer science; Linear-quadratic regulator; Genetic algorithm; Controller (irrigation); Control (management); A priori and a posteriori; Differential evolution; Optimal control; Algorithm; Control theory (sociology); Mathematical optimization; Mathematics; Machine learning; 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":[0.001002456,0.0006982031,0.0004937027,0.0004600339,0.0002484617,0.0005895087,0.000530111,0.0006044691,0.001254123],"category_scores_gemma":[0.001600688,0.0001807881,0.000428717,0.0003123066,0.0003905905,0.0003555439,0.0004919391,0.0004977942,0.0001450955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004406018,"about_ca_system_score_gemma":0.000794211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005868637,"about_ca_topic_score_gemma":0.004218297,"domain_scores_codex":[0.9996568,0.0001185393,0.00001764298,0.00005433473,0.0001106994,0.00004198238],"domain_scores_gemma":[0.9995055,0.0002590883,0.00006207733,0.00003777038,0.0001177833,0.0000177039],"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":[0.00004275114,0.00003809221,0.0005265494,0.00006447484,0.0000282098,0.00003046453,0.00003910914,0.9677336,0.002198105,0.001667657,0.0003712363,0.02725979],"study_design_scores_gemma":[0.00001256573,0.00003787731,0.0001747612,0.000004657441,0.000006677281,0.000008847369,0.000009193713,0.9983953,0.000617691,0.0003602417,0.0003680053,0.000004231215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07364839,0.0005296362,0.9186406,0.0002166687,0.00006576356,0.0000833158,0.00003507122,0.0006016068,0.006179017],"genre_scores_gemma":[0.877708,0.0001931781,0.1197902,0.0001067734,0.00001696426,0.000141288,0.0000942235,0.0000722491,0.001877218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005868637,"threshold_uncertainty_score":0.01166898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005458385265220882,"score_gpt":0.2183048704422495,"score_spread":0.2128464851770286,"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."}}