{"id":"W2765859861","doi":"10.1109/tnnls.2017.2760903","title":"Neural Observer and Adaptive Neural Control Design for a Class of Nonlinear Systems","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":162,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"National Natural Science Foundation of China","keywords":"Backstepping; Control theory (sociology); Nonlinear system; Observer (physics); State observer; Artificial neural network; Computer science; Adaptive control; Bounded function; Controller (irrigation); State variable; Control system; Control engineering; Control (management); Mathematics; Engineering; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005956049,0.0004369956,0.0008572797,0.0001184402,0.0007161992,0.0003322182,0.0002442106,0.0002561054,0.000001419756],"category_scores_gemma":[0.00003239574,0.0003945438,0.0001759768,0.00007210553,0.0001687617,0.0003673582,0.000003511703,0.0007305789,0.000001225183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000456308,"about_ca_system_score_gemma":0.00001279126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002267631,"about_ca_topic_score_gemma":0.00002723304,"domain_scores_codex":[0.9977619,0.0004050017,0.0006678411,0.0004287546,0.0002327052,0.0005038112],"domain_scores_gemma":[0.9979454,0.0009210949,0.0003609473,0.0003938056,0.0001785942,0.0002001381],"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":[0.0003689965,0.00002196145,0.0002626189,0.0001966533,0.000218753,0.00001029103,0.00008810889,0.994423,0.0003483466,0.0000230114,0.00005203097,0.003986223],"study_design_scores_gemma":[0.002345479,0.0007823704,0.0006385142,0.0002257429,0.0001468598,0.00007109736,0.0002560946,0.994687,0.00002051739,5.640528e-7,0.0004686923,0.0003570788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1274219,0.003161754,0.8640289,0.00006309219,0.003253746,0.00173022,0.00007395097,0.0002323143,0.00003404559],"genre_scores_gemma":[0.9984853,0.00007111789,0.0001901272,0.00001592231,0.0006154637,0.0002083462,0.000003134814,0.0001059043,0.0003046358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8710634,"threshold_uncertainty_score":0.9998506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03298311148722489,"score_gpt":0.2372163123545863,"score_spread":0.2042332008673614,"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."}}