{"id":"W2136141574","doi":"10.1109/tmtt.2004.823583","title":"Dynamic Behavioral Modeling of 3G Power Amplifiers Using Real-Valued Time-Delay Neural Networks","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Advanced Power Amplifier Design","field":"Engineering","cited_by":322,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"LDMOS; Time domain; Artificial neural network; Amplifier; Baseband; Behavioral modeling; Waveform; Computer science; Electronic engineering; Nonlinear system; Control theory (sociology); Algorithm; Engineering; Transistor; Telecommunications; Bandwidth (computing); Artificial intelligence; 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":[0.0001829099,0.0004584119,0.0002144771,0.0001712563,0.0001247324,0.0004042134,0.0006009896,0.0005537492,0.000893931],"category_scores_gemma":[0.0004300604,0.000209211,0.0003843719,0.0001536318,0.0002199182,0.0007471994,0.0001425894,0.0004335995,0.0002473607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004397127,"about_ca_system_score_gemma":0.0002519935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003102706,"about_ca_topic_score_gemma":0.003841485,"domain_scores_codex":[0.9999156,0.00001902914,0.000005095161,0.00002246991,0.00002811598,0.000009669978],"domain_scores_gemma":[0.9998894,0.0000490673,0.00002025965,0.00001158634,0.00002636477,0.000003366445],"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":[0.00003198883,0.00001723152,0.0004446848,0.00003603935,0.00002308089,0.00006223313,0.00003183071,0.9661866,0.01079414,0.003470139,0.0001340598,0.01876796],"study_design_scores_gemma":[7.039469e-7,0.000005685167,0.00005253643,0.000001224376,0.000002127778,0.000009624149,0.000001346778,0.9986412,0.0007460323,0.0003663182,0.0001716586,0.000001473738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03597892,0.0002233588,0.9611437,0.00008973003,0.0000284139,0.00001684913,0.00006104671,0.0002776108,0.002180266],"genre_scores_gemma":[0.9072375,0.0005622104,0.08679696,0.00006911724,0.00002625199,0.0001047973,0.000118972,0.00003749044,0.005046634],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003102706,"threshold_uncertainty_score":0.006169319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01316128914943318,"score_gpt":0.2612193728321381,"score_spread":0.248058083682705,"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."}}