{"id":"W2498402313","doi":"10.1007/978-3-319-30379-6_69","title":"Pinning Stabilization of Cellular Neural Networks with Time-Delay Via Delayed Impulses","year":2016,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks Stability and Synchronization","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cellular neural network; Control theory (sociology); Exponential stability; Artificial neural network; Stability (learning theory); Controller (irrigation); Computer science; Exponential growth; Control (management); Mathematics; Physics; Artificial intelligence; Mathematical analysis; Biology; Nonlinear system","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":[0.0002411371,0.0004511151,0.0005288761,0.0001434037,0.0001354672,0.00008953404,0.0007267869,0.0003395545,0.0004733931],"category_scores_gemma":[0.000009232876,0.0003174652,0.0001389465,0.0001533288,0.000181398,0.0005975771,0.0002793175,0.0002679697,0.00002777799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008382171,"about_ca_system_score_gemma":0.00008120794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001336426,"about_ca_topic_score_gemma":0.00002372334,"domain_scores_codex":[0.9976645,0.00006990178,0.0006548361,0.0007468449,0.0004883597,0.0003755711],"domain_scores_gemma":[0.997829,0.0002100071,0.0005132779,0.0009284345,0.00039915,0.0001201508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003181881,0.0001429288,0.0002139441,0.0002577442,0.0004260908,0.0001116577,0.0003599644,0.3347023,0.002230704,0.3722473,0.001850749,0.2871385],"study_design_scores_gemma":[0.0003426645,0.0005029418,0.000005810241,0.0001725613,0.00004650671,0.00002362381,8.610569e-7,0.9911703,0.0003675537,0.005305925,0.001516042,0.0005452427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00007083901,0.0004454524,0.9615895,0.000180637,0.0002238421,0.0004409687,0.000006329281,0.0002367826,0.03680563],"genre_scores_gemma":[0.7561601,0.0003300409,0.02420489,0.0007888016,0.001114137,0.00003113699,0.0002930804,0.0003166418,0.2167612],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9373846,"threshold_uncertainty_score":0.9999278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008138285797632459,"score_gpt":0.1899876847569451,"score_spread":0.1818493989593126,"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."}}