{"id":"W4399060798","doi":"10.1109/jas.2024.124416","title":"Asynchronous Learning-Based Output Feedback Sliding Mode Control for Semi-Markov Jump Systems: A Descriptor Approach","year":2024,"lang":"en","type":"article","venue":"IEEE/CAA Journal of Automatica Sinica","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Outstanding Youth Science Fund Project of National Natural Science Foundation of China; National Natural Science Foundation of China; National Science Foundation","keywords":"Jump; Computer science; Asynchronous communication; Control theory (sociology); Output feedback; Mode (computer interface); Asynchronous learning; Control (management); Markov chain; Hidden Markov model; Artificial intelligence; Mathematics; Machine learning; Physics; Human–computer interaction; Mathematics education; Telecommunications","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":[0.002160422,0.0005458792,0.001385407,0.0005555127,0.0001978411,0.0008186058,0.0005091112,0.0003101593,0.00001940681],"category_scores_gemma":[0.0006944236,0.0004719362,0.0006564519,0.0003580618,0.00006992136,0.0004936481,0.00001536379,0.001126057,0.0000692214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006558664,"about_ca_system_score_gemma":0.0003605563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000636968,"about_ca_topic_score_gemma":8.217724e-7,"domain_scores_codex":[0.9957669,0.0005318365,0.001896123,0.0003724445,0.0006832351,0.0007494364],"domain_scores_gemma":[0.9964568,0.001937391,0.0005104684,0.0003585425,0.0004267279,0.0003100741],"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":[0.0001413798,0.00009812023,0.0001053919,0.002986164,0.001233966,0.00008026339,0.001376352,0.9707774,0.01018201,0.0002763228,0.01113063,0.001611978],"study_design_scores_gemma":[0.002622969,0.0005371247,0.00005931281,0.002214027,0.0003710254,0.0002764551,0.0002548643,0.9785401,0.0003049728,0.00002949608,0.01432213,0.0004674776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0299091,0.006637376,0.9552533,0.0002526121,0.004412513,0.001174849,0.000047463,0.0009270239,0.001385784],"genre_scores_gemma":[0.9897516,0.0000155633,0.008000548,0.00005593543,0.001432427,0.0001218746,0.000007832856,0.0002201592,0.0003940149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9598426,"threshold_uncertainty_score":0.9997732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01291698917640091,"score_gpt":0.2447246408806657,"score_spread":0.2318076517042648,"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."}}