Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/24895
Title: Prediction of air permeability of needle-punched nonwoven fabrics using artificial neural network and empirical models
Authors: Debnath, S
Madhusoothanan, M
Srinivasamoorthy, V R
Keywords: Air permeability;Artificial neural network;Empirical model;Needle-punched nonwoven fabric
Issue Date: Dec-2000
Publisher: NISCAIR-CSIR, India
Abstract: The artificial neural network (ANN) and empirical models have been developed to predict the air permeability of needled fabrics with varying jute and polypropylene blend ratio, fabric weight and needling density. The fabrics were produced as per a statistical factorial design. The efficiency of prediction of the two models has been experimentally verified wherein some of the input variables were beyond the range over which the models were developed. The predicted air permeability values from both the models have been compared statistically. An attempt has also been made to study the effect of number of hidden layer in neural network model. The highest correlation has been found in artificial neural network with three hidden layers. The neural network model with three hidden layers shows less prediction error followed by ANN with two hidden layers, empirical model and ANN with one hidden layer. ANN model with three hidden layers predicts the value of air permeability with minimum error even when the inputs are beyond the range of inputs used for developing the models.
Page(s): 251-255
URI: http://hdl.handle.net/123456789/24895
ISSN: 0975-1025 (Online); 0971-0426 (Print)
Appears in Collections:IJFTR Vol.25(4) [December 2000]

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