{"id":"W2129558416","doi":"10.1109/ssst.2010.5442796","title":"Robust multiple model adaptive control using fuzzy fusion","year":2010,"lang":"en","type":"article","venue":"","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Fuzzy logic; Control theory (sociology); Benchmark (surveying); Fuzzy control system; Controller (irrigation); Robust control; Adaptive control; Adaptive neuro fuzzy inference system; Neuro-fuzzy; Control engineering; Control system; Control (management); Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002864056,0.0001513492,0.0002093102,0.00005952214,0.0001777358,0.0001178385,0.0006517103,0.0001146134,0.000007666299],"category_scores_gemma":[0.00004117913,0.0001142347,0.0000868448,0.0001433109,0.0000377494,0.0004252872,0.0001444544,0.0002094497,0.00005959645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002333149,"about_ca_system_score_gemma":0.0000841813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003143971,"about_ca_topic_score_gemma":0.0001705667,"domain_scores_codex":[0.9987782,0.00004411453,0.0002175604,0.0003819912,0.000257656,0.0003204257],"domain_scores_gemma":[0.9990415,0.00009991683,0.00008230832,0.0005234058,0.0001220652,0.0001307998],"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":[0.00005489272,0.0001321342,0.00125982,0.000006646948,0.00003554711,0.00001928458,0.0003717627,0.2393288,0.1429307,0.6048163,0.0007232077,0.01032103],"study_design_scores_gemma":[0.001018795,0.00003429977,0.000198295,0.0000046201,0.000005143903,0.00001437468,0.00002615946,0.9897362,0.0001604913,0.008562928,0.00007315735,0.0001655239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0279429,0.00002860597,0.9275759,0.000260692,0.000582606,0.0002579237,0.00000261054,0.0002168916,0.0431318],"genre_scores_gemma":[0.8791651,6.386121e-7,0.1197921,0.0004286282,0.000131362,0.00001243761,3.809699e-7,0.000007242485,0.00046207],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8512222,"threshold_uncertainty_score":0.4658358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04202534651061809,"score_gpt":0.2190393757110777,"score_spread":0.1770140292004596,"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."}}