{"id":"W2113620361","doi":"10.1109/isic.2000.882905","title":"Soft computing techniques as applied to expert tuning of PID controllers","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"PID controller; Gain scheduling; Computer science; Soft computing; Control engineering; Process (computing); Identification (biology); Process control; Scheme (mathematics); Control theory (sociology); Control system; Control (management); Engineering; Artificial neural network; Artificial intelligence; Temperature control","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":[0.0008282232,0.0005866689,0.0005849423,0.0005707105,0.0004012941,0.000926158,0.0005741168,0.000671201,0.001766003],"category_scores_gemma":[0.004953551,0.0002513391,0.0003726809,0.0006618098,0.001081586,0.0006551563,0.0007124792,0.001120265,0.0003213109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005109442,"about_ca_system_score_gemma":0.0006117463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001531564,"about_ca_topic_score_gemma":0.00107108,"domain_scores_codex":[0.9993396,0.0001773573,0.00003402352,0.0001028645,0.000281323,0.00006473928],"domain_scores_gemma":[0.9974127,0.001784874,0.0002194164,0.0002163747,0.00029253,0.00007420401],"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":[0.0002681445,0.0001286162,0.000846026,0.0002179365,0.00006068773,0.0001231242,0.0002149953,0.6644213,0.02012597,0.04131788,0.0006816674,0.2715937],"study_design_scores_gemma":[0.00001366362,0.00005240683,0.0001999605,0.000008008149,0.000006626452,0.00002193717,0.00001363711,0.9814361,0.005577789,0.01211237,0.0005498491,0.000007611987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02205061,0.0002868863,0.9737191,0.0001350269,0.00004636074,0.00004107017,0.000009127735,0.0002552834,0.003456452],"genre_scores_gemma":[0.7237819,0.0003975906,0.2723173,0.0001371122,0.00007454137,0.000137491,0.00003035267,0.0000640283,0.003059621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001766003,"threshold_uncertainty_score":0.005907834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008370001236402472,"score_gpt":0.2135483274883057,"score_spread":0.2051783262519032,"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."}}