{"id":"W2005718645","doi":"10.1016/j.apm.2009.09.026","title":"Neural network robust <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si5.gif\" overflow=\"scroll\"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> tracking control strategy for robot manipulators","year":2009,"lang":"lv","type":"article","venue":"Applied Mathematical Modelling","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Artificial neural network; Controller (irrigation); Control theory (sociology); Computer science; Tracking error; Robot; Artificial intelligence; Algorithm; Control (management)","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":[0.0008081652,0.0009406494,0.001002427,0.0004963112,0.0005141838,0.00131175,0.00106039,0.001128667,0.011198],"category_scores_gemma":[0.003772758,0.0004077651,0.0004895691,0.0005366193,0.0005623271,0.001381757,0.0008221787,0.001248536,0.002633859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001042019,"about_ca_system_score_gemma":0.001469059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01218679,"about_ca_topic_score_gemma":0.0087449,"domain_scores_codex":[0.9996064,0.00005988547,0.00002601265,0.0001315604,0.0001274108,0.00004880071],"domain_scores_gemma":[0.9992792,0.0002301176,0.00008783204,0.00009814635,0.000281727,0.00002300725],"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":[0.0002885665,0.00005893737,0.0005163343,0.0002956552,0.00008155501,0.000111247,0.00004463087,0.6817389,0.005677159,0.0228811,0.01321413,0.2750917],"study_design_scores_gemma":[0.00001209087,0.00002706467,0.000193436,0.00001609432,0.00001154651,0.00002235627,0.000006301609,0.9908224,0.001726893,0.005727662,0.001423563,0.00001048788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01128314,0.00091397,0.9702889,0.0007662597,0.0001556115,0.00006989427,0.0002775443,0.001274812,0.01496989],"genre_scores_gemma":[0.7427336,0.001341297,0.195559,0.0003714655,0.0002077306,0.0003671767,0.001920869,0.0004879918,0.0570109],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01218679,"threshold_uncertainty_score":0.0374611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0342071663601374,"score_gpt":0.2422350829069161,"score_spread":0.2080279165467787,"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."}}