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- W2392970622 abstract "The sparse patterns can not meet the requirements of ideal accuracy in training and testing process of BP artificial neural network. To counter this tendency, four steps that improving training or testing results for sparse patterns, determining which are sparse, lone and abnomal patterns by cluster analysis method, selecting lone patterns as training examples, canceling abnomal patterns, and increasing weights of sparse patterns to objective function, have been applied to the modeling yield prediction of FCC unit. naining or testing results both are improved for sparse patterns." @default.
- W2392970622 created "2016-06-24" @default.
- W2392970622 creator A5085411309 @default.
- W2392970622 date "1997-01-01" @default.
- W2392970622 modified "2023-09-23" @default.
- W2392970622 title "STUDY ON IMPROVING TRAINING OR TESTING RESULTS FOR SPARSE PATTERNS IN MODELING YIELD PREDICTION OF FCC UNIT BY BP NEURAL NETWOK" @default.
- W2392970622 hasPublicationYear "1997" @default.
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