{"id":"W1991360560","doi":"10.1109/tnn.2010.2050601","title":"Neural-Network-Based Adaptive Leader-Following Control for Multiagent Systems With Uncertainties","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":340,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Artificial neural network; Multi-agent system; Control theory (sociology); Constraint (computer-aided design); Tracking error; Adaptive control; State (computer science); Control (management); Artificial intelligence; Algorithm; Mathematics","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":[0.0006657045,0.0005820415,0.0005998825,0.0002452273,0.0004534111,0.0005561175,0.001135749,0.0009010797,0.0008429085],"category_scores_gemma":[0.001076481,0.0002451889,0.0003270301,0.0003333843,0.0004897129,0.0006396972,0.0006436546,0.0007322385,0.0001524001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000508869,"about_ca_system_score_gemma":0.0005767665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00611627,"about_ca_topic_score_gemma":0.004841833,"domain_scores_codex":[0.9997455,0.00006600544,0.00001880394,0.00006040755,0.00007334012,0.00003591242],"domain_scores_gemma":[0.9996346,0.0001440576,0.00008451985,0.00001836831,0.0001006627,0.00001777546],"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":[0.00006377949,0.00003651239,0.0002895338,0.00006571252,0.00003462945,0.0001013058,0.00007714994,0.9512359,0.003101868,0.004828881,0.0005174201,0.03964726],"study_design_scores_gemma":[0.000005678553,0.00002014843,0.00004403689,0.00000220908,0.000003409074,0.000006434797,0.000002496756,0.9989873,0.0002279145,0.0005611125,0.0001364776,0.000002815517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02696898,0.0006804476,0.9685026,0.0001765722,0.0001004534,0.00004136705,0.00002144326,0.0002788557,0.003229193],"genre_scores_gemma":[0.9560568,0.0003366784,0.04065612,0.00007817095,0.00006475657,0.0001214889,0.00003748791,0.00001567279,0.002632829],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00611627,"threshold_uncertainty_score":0.01216131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01990881461671435,"score_gpt":0.2318356880729288,"score_spread":0.2119268734562145,"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."}}