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- W3136203418 abstract "In this study, the air permeability of ultrafine glass fiber felts (UGFFs) as a function of bulk density and thickness was predicted by three analysis methods including linear fitting, polynomial fitting, and an artificial neural network (ANN). A 36-set database was obtained by the measurements of samples produced by the flame blowing process. It was shown that the ANN structure with six neurons in the hidden layer was optimal. The ANN model showed much better quality of predicting the permeation rate compared with linear fitting and polynomial fitting, which was evaluated by three important parameters, namely mean relative error (MRE), mean squared error (MSE), and correlation coefficient (R). The prediction diagrams applying the ANN model also matched the theoretical analysis very well, which verified the advantages and practicability of ANN." @default.
- W3136203418 created "2021-03-29" @default.
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- W3136203418 date "2021-03-01" @default.
- W3136203418 modified "2023-10-17" @default.
- W3136203418 title "Predicting the Air Permeability of Ultrafine Glass Fiber Felts: A Comparison of Artificial Neural Network, Linear Fitting, and Polynomial Fitting" @default.
- W3136203418 doi "https://doi.org/10.14504/ajr.8.2.2" @default.
- W3136203418 hasPublicationYear "2021" @default.
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