{"id":"W2805692951","doi":"10.1016/j.amc.2018.05.025","title":"Pinning stochastic sampled-data control for exponential synchronization of directed complex dynamical networks with sampled-data communications","year":2018,"lang":"en","type":"article","venue":"Applied Mathematics and Computation","topic":"Neural Networks Stability and Synchronization","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Synchronization (alternating current); Sampling (signal processing); Computer science; Control theory (sociology); Mathematics; Exponential function; Control (management); Nonlinear system; Mathematical optimization; Applied mathematics; Topology (electrical circuits); Artificial intelligence; Telecommunications; Mathematical analysis","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005126917,0.0001848479,0.000335734,0.00007333144,0.0003806726,0.0001623634,0.001353475,0.00007715067,0.000003960793],"category_scores_gemma":[0.00008245265,0.0001693881,0.00001894645,0.0004002502,0.0002833318,0.0003605969,0.0007779984,0.00009721759,0.000001141279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003013532,"about_ca_system_score_gemma":0.00006255836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002092157,"about_ca_topic_score_gemma":0.000104135,"domain_scores_codex":[0.9984357,0.00004965236,0.000520959,0.0005177116,0.0002398128,0.0002361646],"domain_scores_gemma":[0.9965577,0.00101887,0.0003962298,0.001641862,0.0003189871,0.00006632494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003115448,0.001286752,0.0001274442,0.0007322949,0.0004028739,7.250458e-7,0.003012158,0.2942413,0.001469961,0.4578517,0.001391894,0.2391714],"study_design_scores_gemma":[0.00103965,0.000145125,0.0002564607,0.00006010058,0.0000740313,0.000006584921,0.00004502672,0.9877954,0.000006311788,0.01035008,0.00003895867,0.0001823236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002150711,0.00005546692,0.9961724,0.0003068389,0.00006429877,0.0009689736,0.00008845551,0.0001575096,0.00003530252],"genre_scores_gemma":[0.5980197,0.00000596326,0.4006353,0.0000479929,0.00006311496,0.00002426683,0.001189649,0.00001339781,5.951446e-7],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.693554,"threshold_uncertainty_score":0.6907445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0613466501383422,"score_gpt":0.2956813663481784,"score_spread":0.2343347162098362,"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."}}