{"id":"W2998797223","doi":"10.1016/j.neucom.2020.01.026","title":"Near-optimal neural-network robot control with adaptive gravity compensation","year":2020,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Adaptive Dynamic Programming Control","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cerebellar model articulation controller; Control theory (sociology); Computer science; Artificial neural network; Feed forward; Controller (irrigation); Inverted pendulum; Adaptive control; Lyapunov function; Bounded function; Feedforward neural network; Nonlinear system; Artificial intelligence; Control (management); Mathematics; Control engineering; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004387535,0.0005755814,0.0006217734,0.000275397,0.0004885279,0.0005464648,0.000612393,0.001001889,0.001405117],"category_scores_gemma":[0.001096431,0.0002883135,0.0002402156,0.0003065191,0.000780508,0.0007067103,0.001007041,0.0005581378,0.0002321579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004974508,"about_ca_system_score_gemma":0.0007221086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004812262,"about_ca_topic_score_gemma":0.005198285,"domain_scores_codex":[0.9998218,0.00004127691,0.000007959919,0.00004514395,0.00005239163,0.0000313762],"domain_scores_gemma":[0.9998021,0.00007022479,0.00003872475,0.00001544364,0.00005717425,0.00001632795],"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":[0.0001649375,0.00004126401,0.0002547015,0.00006143654,0.00002647751,0.00006813654,0.0000521525,0.9481383,0.006360237,0.01082574,0.0009407224,0.03306593],"study_design_scores_gemma":[0.000007808295,0.00003060259,0.00008284549,0.000002672261,0.000002682802,0.00001004859,0.000003721782,0.9973682,0.00046631,0.001850621,0.000170661,0.000003817738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05337621,0.000362998,0.937647,0.0003375057,0.00017215,0.00004273767,0.0000289649,0.0002638701,0.007768577],"genre_scores_gemma":[0.9574701,0.00008761302,0.03874562,0.00007558292,0.00004931933,0.00005387242,0.00001984182,0.00002008739,0.003477915],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004812262,"threshold_uncertainty_score":0.009568512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01630035951098792,"score_gpt":0.2153603773335627,"score_spread":0.1990600178225748,"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."}}