{"id":"W2783203055","doi":"10.1002/mma.4736","title":"Global exponential stability of fractional‐order impulsive neural network with time‐varying and distributed delay","year":2018,"lang":"en","type":"article","venue":"Mathematical Methods in the Applied Sciences","topic":"Neural Networks Stability and Synchronization","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Mathematics; Exponential stability; Convergence (economics); Stability (learning theory); Exponential function; Applied mathematics; Artificial neural network; Order (exchange); Lyapunov function; Control theory (sociology); Function (biology); Exponential growth; Current (fluid); Mathematical analysis; Computer science; Artificial intelligence; Nonlinear system; Machine learning","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":[0.0004210819,0.0004199092,0.0003397848,0.0003249689,0.0003459774,0.0005362345,0.0004374837,0.0005162802,0.0007129863],"category_scores_gemma":[0.001144594,0.0001133895,0.0004328136,0.0002428651,0.0007829185,0.0005602649,0.0004841946,0.0005371594,0.00005825803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005396478,"about_ca_system_score_gemma":0.0004021776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001740345,"about_ca_topic_score_gemma":0.0009720016,"domain_scores_codex":[0.999869,0.00002229628,0.000007935872,0.00003557459,0.00004002197,0.0000250299],"domain_scores_gemma":[0.9996476,0.0001684806,0.00007924879,0.00002097246,0.000068467,0.00001510826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001416818,0.00003031602,0.001669348,0.0001513289,0.00007161557,0.000545596,0.0003419544,0.7909128,0.02882025,0.1530181,0.0004109894,0.02388608],"study_design_scores_gemma":[0.000008378737,0.0000368791,0.0003348083,0.000007835482,0.00001768974,0.0000767973,0.00004005515,0.9768093,0.002543005,0.01963878,0.0004767884,0.000009793065],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2119343,0.0007534499,0.7768679,0.0003724892,0.00007525174,0.00001943283,0.00004214176,0.0001158718,0.009819157],"genre_scores_gemma":[0.9925274,0.0002473504,0.005148449,0.00002256129,0.00001198233,0.00001952215,0.00001327421,0.000005399455,0.002004041],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001740345,"threshold_uncertainty_score":0.003915429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03309418404930064,"score_gpt":0.3332977907606336,"score_spread":0.300203606711333,"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."}}