{"id":"W1925341070","doi":"10.1139/p11-059","title":"New results of robust stability analysis for neutral-type neural networks with time-varying delays and Markovian jumping parameters<sup>1</sup>The work of authors was supported by Department of Science and Technology, New Delhi, India, under the sanctioned No. SR/S4/MS:485/07.","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Physics","topic":"Neural Networks Stability and Synchronization","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stability (learning theory); Artificial neural network; Work (physics); Linear matrix inequality; Type (biology); Jumping; Stability criterion; Control theory (sociology); Applied mathematics; Physics; Computer science; Mathematics; Mathematical optimization; Artificial intelligence; Machine learning; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001612284,0.001553495,0.0006681949,0.0007748281,0.0003726764,0.0009674213,0.0009188639,0.0007757336,0.002853849],"category_scores_gemma":[0.00248369,0.0002911884,0.00116762,0.0004374914,0.0008137054,0.001362889,0.0008562951,0.001406721,0.0004291723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007922503,"about_ca_system_score_gemma":0.0006363046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008702741,"about_ca_topic_score_gemma":0.0007250054,"domain_scores_codex":[0.9995847,0.000105095,0.00003601172,0.00009916297,0.0001439657,0.00003111826],"domain_scores_gemma":[0.9993488,0.0002842642,0.0001070782,0.00002937301,0.0002067721,0.00002361137],"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":[0.0001461125,0.0000590286,0.0007297242,0.0008832317,0.0002928525,0.0005349479,0.0005728668,0.5198582,0.02598546,0.3829869,0.003371856,0.06457886],"study_design_scores_gemma":[0.0000088095,0.00007595717,0.0002012328,0.00003680587,0.00004783916,0.0001296054,0.00004599217,0.9463921,0.002371491,0.04793691,0.002733831,0.00001946452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007821511,0.001432353,0.9819736,0.0001988209,0.0001369397,0.0000345523,0.00005333182,0.00007635767,0.008272386],"genre_scores_gemma":[0.8336082,0.005206824,0.1474774,0.0003184819,0.0004084543,0.0003282627,0.0003012776,0.0001523307,0.01219873],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002853849,"threshold_uncertainty_score":0.009547055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02478310143903127,"score_gpt":0.211484518085995,"score_spread":0.1867014166469637,"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."}}