{"id":"W1594439527","doi":"10.1108/ijicc-11-2014-0046","title":"Auto-regressive multiple-valued logic neurons with sequential Chua’s oscillator back-propagation learning for online prediction and synchronization of chaotic trajectories","year":2015,"lang":"en","type":"article","venue":"International Journal of Intelligent Computing and Cybernetics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Chaotic; Artificial neural network; Nonlinear system; Context (archaeology); Sequence (biology); Trigonometric functions; Attractor; Class (philosophy); Synchronization (alternating current); Function (biology); Artificial intelligence; Algorithm; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0002236051,0.0001127672,0.0001613372,0.0001074058,0.00006618193,0.0001071813,0.0002409294,0.00004246799,0.000001290818],"category_scores_gemma":[0.0001568879,0.00008910586,0.00003928694,0.0001144722,0.00008106475,0.0001609195,0.00008618275,0.0001483227,3.224681e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005004608,"about_ca_system_score_gemma":0.00008429265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001004534,"about_ca_topic_score_gemma":0.000006089161,"domain_scores_codex":[0.9989117,0.00005209697,0.0004273491,0.0001691247,0.0003304917,0.0001091863],"domain_scores_gemma":[0.9980701,0.0001578968,0.0005632609,0.0000724223,0.001046171,0.00009013981],"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":[0.000347731,0.0004376196,0.02249643,0.00008709252,0.0003716793,0.00001976136,0.003573291,0.6940489,0.002883109,0.02710891,0.0003836135,0.2482418],"study_design_scores_gemma":[0.0006892691,0.0008065323,0.002851003,0.0001863059,0.00003021722,0.0002004441,0.00013147,0.9921586,0.001565459,0.0007415148,0.0005440349,0.00009514031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3231565,0.000177689,0.6758042,0.0003363237,0.0003813592,0.0001114551,0.000005179907,0.00001414894,0.00001313814],"genre_scores_gemma":[0.962459,0.0001118661,0.03700858,0.00003525991,0.0003277286,0.000001143319,0.0000149825,0.000008645271,0.00003277502],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6393025,"threshold_uncertainty_score":0.3633631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03263782837959502,"score_gpt":0.2859845960889061,"score_spread":0.2533467677093111,"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."}}