{"id":"W4205314198","doi":"10.21203/rs.3.rs-719634/v1","title":"Adaptive Neural Backstepping Control of Nonlinear Fractional-Order Systems with Input Quantization","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Backstepping; Nonlinear system; Control theory (sociology); Quantization (signal processing); Computer science; Artificial neural network; Adaptive control; Mathematics; Control (management); Artificial intelligence; Physics; Algorithm","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":[0.0004840003,0.0003273471,0.0003720157,0.0001396983,0.0002375918,0.0004557141,0.000489424,0.0004449808,0.0006144221],"category_scores_gemma":[0.001161789,0.000124723,0.0002061674,0.0001800629,0.0004589256,0.000334772,0.0003875712,0.0004155088,0.00005159151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003092979,"about_ca_system_score_gemma":0.0003231893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004436675,"about_ca_topic_score_gemma":0.002479224,"domain_scores_codex":[0.9997947,0.00004820997,0.00001297264,0.00004014809,0.00008204281,0.0000219699],"domain_scores_gemma":[0.9997398,0.0001176305,0.00004381423,0.00002085762,0.00006698928,0.00001082383],"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":[0.000252155,0.00008908103,0.0007791255,0.0002696317,0.00005697695,0.0002662534,0.0002341817,0.8562952,0.05044506,0.00926453,0.0004932868,0.08155458],"study_design_scores_gemma":[0.00000689014,0.0000347252,0.00009224692,0.000003188058,0.000003561287,0.000008242014,0.000004153877,0.9979277,0.001387402,0.000357917,0.0001715396,0.000002449201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1143576,0.0003852532,0.8817787,0.0001487362,0.0001147827,0.00003918052,0.00001474483,0.0002436408,0.002917347],"genre_scores_gemma":[0.9830504,0.00008897031,0.01575144,0.0000262835,0.00001274946,0.00002437289,0.000008156268,0.000004613626,0.001033029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004436675,"threshold_uncertainty_score":0.008821666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04698569602430403,"score_gpt":0.3230422078416426,"score_spread":0.2760565118173385,"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."}}