{"id":"W2133815830","doi":"10.1162/neco.2007.19.6.1589","title":"A Measurement Fusion Method for Nonlinear System Identification Using a Cooperative Learning Algorithm","year":2007,"lang":"en","type":"article","venue":"Neural Computation","topic":"Control Systems and Identification","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Algorithm; Nonlinear system; Fusion; Estimator; Function (biology); Support vector machine; Noise (video); Computer science; Sensor fusion; Mean squared error; Observational error; Artificial intelligence; Mathematics; Statistics","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":[0.001652873,0.00078015,0.001132386,0.0007500648,0.0006700027,0.0006488094,0.001176056,0.001271845,0.001020269],"category_scores_gemma":[0.003027255,0.0003914041,0.0008103179,0.0009043446,0.0008420399,0.001633783,0.001397516,0.001048154,0.0004193212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004671517,"about_ca_system_score_gemma":0.0007161777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001524161,"about_ca_topic_score_gemma":0.0008470644,"domain_scores_codex":[0.9986529,0.0003765696,0.00008167604,0.0003116319,0.0005155577,0.00006157075],"domain_scores_gemma":[0.9990112,0.0003896063,0.000141154,0.0001724155,0.0002553834,0.00003032187],"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":[0.0001996775,0.0001285393,0.0009460131,0.0001949727,0.0001734676,0.0002075605,0.0003275568,0.4707442,0.03277568,0.0419464,0.001266528,0.4510894],"study_design_scores_gemma":[0.000007562109,0.00005478601,0.0001351545,0.000004614599,0.00001197084,0.00005542515,0.000008151317,0.9926136,0.0033626,0.002890795,0.0008413542,0.00001401347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002268464,0.00007959522,0.9972384,0.00002357576,0.00001382918,0.00001027649,0.000002676108,0.00009563913,0.0002674973],"genre_scores_gemma":[0.4726625,0.0003023931,0.5244552,0.00009592027,0.0001039429,0.0002069655,0.00005736724,0.00005270312,0.002063038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001652873,"threshold_uncertainty_score":0.008741319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03364833304549326,"score_gpt":0.2965821212218306,"score_spread":0.2629337881763373,"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."}}