{"id":"W2492514055","doi":"10.4018/978-1-4666-8823-0.ch005","title":"On Simulation Performance of Feedforward and NARX Networks Under Different Numerical Training Algorithms","year":2015,"lang":"en","type":"book-chapter","venue":"Advances in systems analysis, software engineering, and high performance computing book series","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Nonlinear autoregressive exogenous model; Conjugate gradient method; Broyden–Fletcher–Goldfarb–Shanno algorithm; Backpropagation; Algorithm; Computer science; Artificial neural network; Nonlinear conjugate gradient method; Rprop; Autoregressive model; Levenberg–Marquardt algorithm; Feedforward neural network; Nonlinear system; Moving average; Autoregressive–moving-average model; Feed forward; Artificial intelligence; Gradient descent; Mathematics; Recurrent neural network; Engineering; Econometrics; Types of artificial neural networks; Control engineering","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":[0.0002539845,0.0005126529,0.0010569,0.000398702,0.0001884052,0.0001269046,0.0003476292,0.0002273798,0.00000279882],"category_scores_gemma":[0.00001214388,0.000463684,0.00009473473,0.0002804354,0.0001101692,0.0008164538,0.0002025398,0.0004759787,8.741465e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009279743,"about_ca_system_score_gemma":0.00002908731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009918621,"about_ca_topic_score_gemma":0.000004365178,"domain_scores_codex":[0.9977922,0.00001974008,0.0007683047,0.0006513612,0.0003889131,0.0003795035],"domain_scores_gemma":[0.9985029,0.0003237321,0.0004721027,0.0004420501,0.0001239587,0.0001352235],"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":[0.00001144645,0.000008925276,0.0007842777,0.0002078215,0.0001219338,0.00000171051,0.0001743885,0.9590808,2.976242e-7,0.02632164,0.000007266229,0.01327947],"study_design_scores_gemma":[0.0002109224,0.0002429853,0.002132824,0.0006594944,0.0001031032,0.00001133185,0.00001360501,0.9882621,0.000002008071,0.0001091798,0.007773109,0.000479316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0288973,0.02249143,0.9470471,0.00001734307,0.0005611174,0.000389689,0.00001299244,0.0002574979,0.0003255036],"genre_scores_gemma":[0.9883763,0.006269705,0.003373598,0.00002206103,0.000226929,0.00001745968,0.00004462858,0.00004185156,0.001627495],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.959479,"threshold_uncertainty_score":0.9997815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01349791285612402,"score_gpt":0.2312183587665233,"score_spread":0.2177204459103992,"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."}}