{"id":"W2134254077","doi":"10.1109/ccece.2008.4564682","title":"Implementation of the RBF neural network on a SOPC for maximum power point tracking","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Photovoltaic System Optimization Techniques","field":"Energy","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Maximum power point tracking; PID controller; Pulse-width modulation; Duty cycle; Field-programmable gate array; Computer science; Artificial neural network; Photovoltaic system; Gate array; Maximum power principle; Controller (irrigation); Programmable logic controller; Electronic engineering; Engineering; Control theory (sociology); Control engineering; Embedded system; Electrical engineering; Voltage; Artificial intelligence; Control (management); Temperature control","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.000432898,0.0004221471,0.000373501,0.0003121191,0.0003033873,0.0004380585,0.0007259045,0.0006857799,0.002788384],"category_scores_gemma":[0.001059699,0.0001884676,0.0002857884,0.0002803456,0.0001968294,0.0005189355,0.0002308835,0.000659486,0.0009481718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003420957,"about_ca_system_score_gemma":0.0005813661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004347463,"about_ca_topic_score_gemma":0.003694171,"domain_scores_codex":[0.9996585,0.00007824453,0.00001655754,0.00006184601,0.0001540501,0.00003081748],"domain_scores_gemma":[0.9996459,0.0000814323,0.00002389395,0.00004880143,0.0001849936,0.00001491449],"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":[0.0005446288,0.0002442329,0.001323314,0.0002497183,0.0001053111,0.0001974734,0.0001537476,0.2279777,0.1195308,0.01271254,0.004319884,0.6326407],"study_design_scores_gemma":[0.00002746902,0.0001377456,0.0005721529,0.00001039356,0.00002295825,0.0000776907,0.000008999416,0.9670148,0.02691832,0.0006587289,0.00453641,0.00001423067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01664241,0.0001137254,0.9772485,0.00008900442,0.00007948223,0.0000586143,0.00002465725,0.001763203,0.003980292],"genre_scores_gemma":[0.4898756,0.0002015006,0.5029786,0.0001363516,0.00004816521,0.0002162098,0.0001256585,0.0001354529,0.006282454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004347463,"threshold_uncertainty_score":0.009328127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01945271371692893,"score_gpt":0.2201143367086988,"score_spread":0.2006616229917699,"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."}}