{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003773659,0.0002547521,0.0006377054,0.00008994268,0.0001208281,0.00007631543,0.0000615974,0.00009296898,0.00002954608],"category_scores_gemma":[0.00000847908,0.0002231312,0.0000333048,0.0001068204,0.00002293791,0.0003388645,0.00004378521,0.0001049692,0.000007504384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001278944,"about_ca_system_score_gemma":0.00001246206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001075432,"about_ca_topic_score_gemma":0.000004833143,"domain_scores_codex":[0.9987214,0.00007898555,0.0003946168,0.0003260835,0.0001924769,0.0002864758],"domain_scores_gemma":[0.9994009,0.0001471136,0.0001072432,0.0001519099,0.00008388485,0.0001089146],"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":[0.0001216559,0.000009559782,0.0004817395,0.0002694509,0.0002554039,0.000002464834,0.0001272462,0.8620862,0.1356938,0.0006471291,0.0000275875,0.0002777028],"study_design_scores_gemma":[0.005322075,0.00004333028,0.00003993705,0.000221539,0.00006418773,0.00007474159,0.000202334,0.9921117,0.00158868,0.000006952692,0.00004092617,0.0002836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08578921,0.0003725593,0.9101411,0.000007615897,0.0001115362,0.0006550152,0.00000696711,0.0003729004,0.002543097],"genre_scores_gemma":[0.6576127,0.00001730425,0.34145,0.00000786179,0.00004749848,0.00002763598,0.00001544305,0.00006143053,0.0007600224],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5718235,"threshold_uncertainty_score":0.9099026,"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."}}