{"id":"W2992239498","doi":"10.1016/j.neucom.2019.11.055","title":"Event-triggering based adaptive neural tracking control for a class of pure-feedback systems with finite-time prescribed performance","year":2019,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lakehead University","funders":"National Natural Science Foundation of China","keywords":"Control theory (sociology); Tracking error; Nonlinear system; Controller (irrigation); Artificial neural network; Lyapunov function; Lyapunov stability; Bounded function; Mean value theorem (divided differences); Computer science; Tracking (education); Uniform boundedness; Adaptive control; Mathematics; Stability (learning theory); Function (biology); Boundary (topology); Control (management); 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":[0.0007190041,0.0008849651,0.0006340131,0.0003283209,0.0003975845,0.0009598186,0.000988483,0.0007475897,0.001199688],"category_scores_gemma":[0.001903366,0.0001733652,0.000352913,0.0002556313,0.0006504668,0.0006131475,0.000842997,0.0005621904,0.0001422479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005819008,"about_ca_system_score_gemma":0.0006815679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0017785,"about_ca_topic_score_gemma":0.001781833,"domain_scores_codex":[0.9997188,0.00005199001,0.00001590746,0.00006961545,0.00009480106,0.00004887732],"domain_scores_gemma":[0.9992427,0.0003518966,0.0001482183,0.00005218794,0.0001554446,0.00004969911],"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":[0.0008500351,0.0002732849,0.00183218,0.0004792598,0.0001313065,0.0008047494,0.000316199,0.6688449,0.05486962,0.1941762,0.002037221,0.07538505],"study_design_scores_gemma":[0.00001248599,0.00004251588,0.0002108452,0.000003702009,0.00000757763,0.0000301266,0.000003973733,0.9940951,0.0009633458,0.004469546,0.0001561409,0.000004552324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.155145,0.0004929727,0.8353865,0.0001939251,0.0001053911,0.00008303513,0.00008715487,0.0003242468,0.00818194],"genre_scores_gemma":[0.9871743,0.0001607222,0.009506973,0.00003713816,0.00002773576,0.00004167603,0.00003511442,0.00001319793,0.003003096],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0017785,"threshold_uncertainty_score":0.004222035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0115420450700202,"score_gpt":0.1987289639610224,"score_spread":0.1871869188910021,"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."}}