{"id":"W2014549417","doi":"10.1016/s0893-6080(02)00025-4","title":"Exponential stability of Cohen–Grossberg neural networks","year":2002,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks Stability and Synchronization","field":"Computer Science","cited_by":239,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Exponential stability; Monotonic function; Differentiable function; Mathematics; Applied mathematics; Artificial neural network; Exponential function; Stability (learning theory); Connection (principal bundle); Monotone polygon; Matrix (chemical analysis); Pure mathematics; Mathematical analysis; Computer science; Physics; Artificial intelligence; Nonlinear system; Quantum mechanics; Geometry","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":[0.0004619599,0.0004408434,0.0005841858,0.00009067936,0.0003356342,0.0002244572,0.001419462,0.000303914,0.0004122449],"category_scores_gemma":[0.00006988949,0.0004169679,0.0003124271,0.001192804,0.0003237251,0.001145941,0.0005525011,0.0007154698,0.000009660061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007904734,"about_ca_system_score_gemma":0.00001207129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004394724,"about_ca_topic_score_gemma":0.00006142777,"domain_scores_codex":[0.9960188,0.0004910314,0.0009609989,0.0009654702,0.0005914774,0.0009722196],"domain_scores_gemma":[0.9972963,0.0004304722,0.0003767983,0.001382541,0.0002292151,0.0002846426],"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":[0.00006391914,0.0003552293,0.01093963,0.00003894288,0.00003391604,0.00003334794,0.0003508479,0.8897139,0.0001905719,0.002380086,0.003017018,0.09288252],"study_design_scores_gemma":[0.0005682612,0.0002361789,0.002853675,0.00001584489,0.00001769826,0.00003124603,0.000010271,0.9952649,0.000164908,0.0002146381,0.0002415483,0.0003807573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3405856,0.002053238,0.6508479,0.001088043,0.003506695,0.0007021126,0.000006258052,0.0005311576,0.0006789967],"genre_scores_gemma":[0.9976428,0.0001185191,0.0008280938,0.0005061098,0.0007681238,0.00002532125,0.0000197067,0.0000330063,0.00005828627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6570572,"threshold_uncertainty_score":0.9998282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02493626628344455,"score_gpt":0.2198510948833397,"score_spread":0.1949148285998952,"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."}}