{"id":"W3082164767","doi":"10.3390/mi11090831","title":"A Time Delay Neural Network Based Technique for Nonlinear Microwave Device Modeling","year":2020,"lang":"en","type":"article","venue":"Micromachines","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Science Foundation of Tianjin City; Tianjin University; Shaanxi University of Science and Technology; Beijing University of Technology; National Natural Science Foundation of China","keywords":"MESFET; Artificial neural network; Computer science; Nonlinear system; Microwave; High-electron-mobility transistor; Electronic engineering; Generalization; SIGNAL (programming language); Transistor; Engineering; Artificial intelligence; Electrical engineering; Field-effect transistor; Telecommunications; Physics","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":[0.0002198014,0.0004418704,0.0002069037,0.0002657474,0.0002261021,0.0003007872,0.0005743366,0.0004502334,0.001733012],"category_scores_gemma":[0.00051339,0.0002054958,0.0003786199,0.0003625527,0.000209275,0.0007802285,0.0002469918,0.0007678216,0.0003790348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004777748,"about_ca_system_score_gemma":0.0004091347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003974114,"about_ca_topic_score_gemma":0.004899174,"domain_scores_codex":[0.9999239,0.0000168732,0.000005167418,0.00001814764,0.00002968022,0.000006281335],"domain_scores_gemma":[0.9999018,0.00004196985,0.00001494078,0.000009523649,0.00002787404,0.000003922774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003834683,0.0000328813,0.0004628845,0.0001092427,0.00004321168,0.00007814402,0.00004506475,0.8432592,0.02230262,0.0199226,0.0009246122,0.1127812],"study_design_scores_gemma":[9.258068e-7,0.000007598695,0.00003439893,0.000002466135,0.000003107979,0.00001525045,0.000001681038,0.9965269,0.00153725,0.0009510498,0.0009172063,0.000002187028],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002766207,0.0001654454,0.9955018,0.00005208472,0.00002852051,0.00001262241,0.00002643448,0.0001512179,0.001295538],"genre_scores_gemma":[0.3754135,0.001302131,0.6109684,0.0001303796,0.00007535585,0.0002161098,0.0002424885,0.0001196115,0.01153203],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003974114,"threshold_uncertainty_score":0.007901967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01402239283536402,"score_gpt":0.2177665058832938,"score_spread":0.2037441130479297,"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."}}