{"id":"W2794195661","doi":"10.1109/tsmc.2018.2791575","title":"Finite-Horizon $H_\\infty$ State Estimation for Time-Varying Neural Networks with Periodic Inner Coupling and Measurements Scheduling","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Neural Networks Stability and Synchronization","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Fundamental Research Funds for the Central Universities; China National Funds for Distinguished Young Scientists; National Natural Science Foundation of China","keywords":"Estimator; Markov chain; Artificial neural network; Scheduling (production processes); Mathematics; Applied mathematics; Coupling (piping); Algorithm; Computer science; Mathematical optimization; Discrete mathematics; Artificial intelligence; Statistics; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000599921,0.0002964507,0.0003540911,0.000146336,0.0007138125,0.0008403412,0.0002045244,0.0001271866,0.000001578269],"category_scores_gemma":[0.000007782864,0.0002607658,0.00004279411,0.0003316538,0.0001342577,0.0004626584,0.000006410013,0.0001923685,0.000005796496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008615018,"about_ca_system_score_gemma":0.00003701854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001011742,"about_ca_topic_score_gemma":0.00004058269,"domain_scores_codex":[0.9979749,0.0001218853,0.0005245762,0.000599922,0.0003792409,0.0003994753],"domain_scores_gemma":[0.9987183,0.0002146214,0.0002275941,0.0003763914,0.0003020924,0.0001609734],"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":[0.00006050576,0.00003587612,0.000129914,0.00019736,0.00004715884,0.000001493243,0.0005268747,0.9930524,0.0002208847,0.0001127354,0.00001028685,0.005604571],"study_design_scores_gemma":[0.0007657616,0.0007931001,0.00003126682,0.0003901024,0.00004085031,0.00004117209,0.00008030592,0.9972596,0.0002233379,0.00001606576,0.00005923638,0.0002992633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1041658,0.0004322449,0.8929721,0.00004617874,0.001148926,0.001023386,0.000008969737,0.0001590803,0.000043322],"genre_scores_gemma":[0.9969795,0.00003072954,0.002442379,0.00002673013,0.0001620477,0.0001250927,0.00000606442,0.00003502096,0.0001924689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8928137,"threshold_uncertainty_score":0.9999844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02294495891372567,"score_gpt":0.2318019055784537,"score_spread":0.208856946664728,"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."}}