{"id":"W2166958963","doi":"10.1142/s0218488505003746","title":"FUZZY RULE EXTRACTION FROM A FEED FORWARD NEURAL NETWORK BY TRAINING A REPRESENTATIVE FUZZY NEURAL NETWORK USING GRADIENT DESCENT","year":2005,"lang":"en","type":"article","venue":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Artificial neural network; Gradient descent; Defuzzification; Fuzzy number; Fuzzy logic; Transformation (genetics); Measure (data warehouse); Membership function; Mathematics; Neuro-fuzzy; Artificial intelligence; Fuzzy classification; Computer science; Function (biology); Fuzzy set; Pattern recognition (psychology); Data mining; Fuzzy control 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":[0.0007281645,0.0003509771,0.000548101,0.0001693079,0.0003441417,0.000627926,0.0009578695,0.000115225,0.000006551902],"category_scores_gemma":[0.0000444924,0.0002963198,0.0002680525,0.0004786433,0.00009981218,0.000806023,0.0001563579,0.0004461353,0.000004232231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000326769,"about_ca_system_score_gemma":0.000201069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003433613,"about_ca_topic_score_gemma":0.0001126997,"domain_scores_codex":[0.9968484,0.0003317501,0.001127062,0.0005097633,0.0006575008,0.0005254947],"domain_scores_gemma":[0.9968503,0.0005368788,0.001079813,0.0002987447,0.0009113074,0.0003230268],"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":[0.0002372789,0.0002120039,0.001755476,0.00001463728,0.0002158505,0.00004665341,0.0007120857,0.9461546,0.001580552,0.002418764,0.006571381,0.04008071],"study_design_scores_gemma":[0.001862823,0.000129355,0.001239383,0.0004279418,0.00006523082,0.0002464143,0.0003036784,0.9855117,0.00009553222,0.001586917,0.008162789,0.0003682322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7514489,0.007764774,0.227648,0.003843841,0.008008058,0.00064197,0.00006460088,0.0001107801,0.0004690276],"genre_scores_gemma":[0.9899375,0.00007590577,0.004353351,0.0002625181,0.005216609,0.00003224327,0.00003376179,0.00002700658,0.00006110098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2384886,"threshold_uncertainty_score":0.9999489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03643321490396093,"score_gpt":0.3062558588690791,"score_spread":0.2698226439651182,"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."}}