{"id":"W2119248427","doi":"10.1109/iscas.2006.1693201","title":"A Full-Differential Analog Design of an Indirect Inverse Control Law Based on Neural Networks","year":2006,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; CMC Microsystems","keywords":"Computer science; Artificial neural network; Differential (mechanical device); Inverse; Analogue electronics; Control theory (sociology); Electronic circuit; Control engineering; Control (management); Electronic engineering; Engineering; Artificial intelligence; Mathematics; Electrical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003605688,0.0004020245,0.0002883897,0.000226117,0.0002589663,0.0005772923,0.0009088719,0.0005867587,0.00169161],"category_scores_gemma":[0.0007114194,0.0001972702,0.0002724046,0.0001330461,0.0004430573,0.0004604441,0.0003777018,0.0004807823,0.0002839307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003567529,"about_ca_system_score_gemma":0.0003759409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009791744,"about_ca_topic_score_gemma":0.001170897,"domain_scores_codex":[0.999742,0.00003247535,0.00001669844,0.00005962209,0.000131469,0.0000177561],"domain_scores_gemma":[0.9998072,0.00006057015,0.00002112618,0.00002407082,0.00007936559,0.000007708896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002501816,0.0001752131,0.0007311867,0.0005922228,0.0001141222,0.0003743311,0.0003168681,0.2790938,0.2241014,0.06495783,0.002526645,0.4267662],"study_design_scores_gemma":[0.00005284923,0.0003187434,0.0003762017,0.00003749034,0.00004953862,0.0002543139,0.0000117523,0.9448814,0.03954875,0.004016618,0.01042702,0.00002524935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007871404,0.0001811442,0.9861197,0.00009027463,0.0001009765,0.00005816214,0.00001521191,0.0003776014,0.005185508],"genre_scores_gemma":[0.7534732,0.0003203655,0.2398057,0.0002322151,0.00006964494,0.0001938297,0.00004497983,0.00004107672,0.005819097],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00169161,"threshold_uncertainty_score":0.005658984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01492862633367079,"score_gpt":0.2196529210639966,"score_spread":0.2047242947303258,"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."}}