{"id":"W4360848666","doi":"10.1177/01423312231155375","title":"Adaptive neural backstepping control of nonlinear fractional-order systems with input quantization","year":2023,"lang":"en","type":"article","venue":"Transactions of the Institute of Measurement and Control","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Backstepping; Control theory (sociology); Nonlinear system; Artificial neural network; Bounded function; Quantization (signal processing); Controller (irrigation); Computer science; Lyapunov stability; Lyapunov function; Mathematics; Observer (physics); Adaptive control; Control (management); Algorithm; 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":[0.0004322178,0.0001832047,0.0004607033,0.0001649104,0.00008723723,0.0000108964,0.0001577441,0.0000755881,0.00000388861],"category_scores_gemma":[0.00003431043,0.0001361784,0.0001075464,0.0004096648,0.000155266,0.0002501764,0.00000350648,0.0001513533,0.000001744777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004966968,"about_ca_system_score_gemma":0.00008336418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001553202,"about_ca_topic_score_gemma":0.0001529428,"domain_scores_codex":[0.998484,0.00007129811,0.00052972,0.0001417941,0.000610713,0.0001625015],"domain_scores_gemma":[0.9987663,0.000073858,0.0002279247,0.0002335311,0.0006544273,0.00004395549],"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":[0.0003057354,0.00004994082,0.0006369515,0.0002358054,0.0007484137,8.777761e-7,0.00009877032,0.9676195,0.02904491,0.0001932443,0.00001355023,0.001052281],"study_design_scores_gemma":[0.004540646,0.0001925276,0.002988813,0.0004413125,0.0003877382,0.000008047785,0.0002579462,0.9886397,0.001785723,0.00000820133,0.0005768248,0.0001724973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04357477,0.001039993,0.9527588,0.0002272326,0.0009459295,0.001005699,0.0001252815,0.0001008415,0.0002214158],"genre_scores_gemma":[0.9994832,0.00003320968,0.000306577,0.000008858595,0.00006792105,0.0000422085,0.000002748109,0.0000243884,0.00003089516],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9559084,"threshold_uncertainty_score":0.5553193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02625194178716096,"score_gpt":0.2091086545129038,"score_spread":0.1828567127257428,"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."}}