{"id":"W2049570504","doi":"10.1109/imws.2009.4814911","title":"Neural Network Techniques for High-Speed Electronic Component Modeling","year":2009,"lang":"en","type":"article","venue":"","topic":"Induction Heating and Inverter Technology","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Artificial neural network; Computer science; Component (thermodynamics); Nonlinear system; Physical neural network; Biological neural network; Electronic engineering; Electronic circuit; Time delay neural network; Artificial intelligence; Control engineering; Types of artificial neural networks; Machine learning; Engineering; Electrical engineering","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":[0.0004832266,0.0008231276,0.0006365254,0.0005793193,0.0003313867,0.0006735977,0.0009429136,0.001018203,0.003354312],"category_scores_gemma":[0.001279769,0.0004282711,0.0006346739,0.0009464343,0.0003419339,0.001030803,0.0004885603,0.001460031,0.001087654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006126956,"about_ca_system_score_gemma":0.0004770661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005319389,"about_ca_topic_score_gemma":0.004370131,"domain_scores_codex":[0.9997553,0.00007026632,0.00001743749,0.00003310402,0.0001092428,0.00001459548],"domain_scores_gemma":[0.9997408,0.0001494724,0.00002146739,0.00002243451,0.00006007074,0.000005777251],"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":[0.00002412405,0.00001841411,0.0002067752,0.0001408391,0.00005475033,0.00007865649,0.00003410758,0.846232,0.003050131,0.02979024,0.001382289,0.1189876],"study_design_scores_gemma":[0.000002372634,0.000005950847,0.00005058297,0.00001042359,0.000005274989,0.00001453772,0.000002590447,0.9887491,0.0004286261,0.008708147,0.002018186,0.000004233709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008518757,0.0008789008,0.9961057,0.00007657958,0.00002909168,0.00001200746,0.00002694418,0.0002272398,0.001791599],"genre_scores_gemma":[0.2964283,0.008037884,0.6769488,0.0001788866,0.0002261256,0.0005191279,0.0003984836,0.0002334244,0.01702897],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005319389,"threshold_uncertainty_score":0.01122129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01347154703338853,"score_gpt":0.2218880150642162,"score_spread":0.2084164680308276,"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."}}