{"id":"W2015975537","doi":"10.1049/iet-cta.2012.0343","title":"Fuzzy logic‐based adaptive gravitational search algorithm for optimal tuning of fuzzy‐controlled servo systems","year":2013,"lang":"en","type":"article","venue":"IET Control Theory and Applications","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Autoritatea Natională pentru Cercetare Stiintifică","keywords":"Control theory (sociology); Fuzzy logic; Mathematics; Sensitivity (control systems); Servo; PID controller; Servomechanism; Fuzzy control system; Algorithm; Computer science; Engineering; Control engineering; Artificial intelligence; Control (management); Temperature control","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001561657,0.000235068,0.0005941823,0.0001311746,0.000347895,0.0001917451,0.0006211607,0.000123149,0.000004720446],"category_scores_gemma":[0.00005226493,0.0001889646,0.0001881311,0.0002508982,0.0001702902,0.0003249757,0.00005633733,0.0001369492,0.00002538604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002917096,"about_ca_system_score_gemma":0.0001220118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004454665,"about_ca_topic_score_gemma":8.240913e-7,"domain_scores_codex":[0.9978431,0.0004348669,0.0005564594,0.0004948952,0.0002899856,0.0003806941],"domain_scores_gemma":[0.9961053,0.002351251,0.0002765885,0.0004732586,0.0006310098,0.0001626291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001547778,0.0001017382,0.00001543849,0.00002996079,0.0001240789,4.806174e-7,0.00009350303,0.004849504,0.001588024,0.9703447,0.00008491265,0.02261286],"study_design_scores_gemma":[0.00967409,0.0003399957,0.0002427749,0.00003645727,0.00007690286,0.000009480565,0.0005922084,0.5806593,0.00005389894,0.4075293,0.0004747507,0.0003108883],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004391187,0.001138766,0.9891518,0.0006202343,0.00009603088,0.004184764,0.0001204218,0.0001054424,0.004143377],"genre_scores_gemma":[0.9589916,0.000006170406,0.03211616,0.0003539648,0.000227053,0.007912764,0.00002218073,0.00001491302,0.0003551507],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9585525,"threshold_uncertainty_score":0.7705753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01666008227689332,"score_gpt":0.250119874006745,"score_spread":0.2334597917298517,"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."}}