Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/24599
Title: Application of linear regression, artificial neural network and neuro-fuzzy algorithms to predict the breaking elongation of rotor-spun yarns
Authors: Majumdar, Abhijit
Majumdar, Prabal Kumar
Sarkar, Bijan
Keywords: Artificial neural network;Breaking elongation;Cotton fibre;High volume instrument;Neuro-fuzzy model;Rotor-spun yarns
Issue Date: Mar-2005
Publisher: NISCAIR-CSIR, India
IPC Code: Int. CI.7D02G3/00; G06N3/02; G06N7/02
Abstract: The breaking elongation of rotor-spun yarns has been predicted by using linear regression, artificial neural network and neuro-fuzzy models. Cotton fibre properties measured by high volume instrument and yarn count have been used as inputs to the prediction models. Prediction accuracy is found to be better for artificial neural network and neuro-fuzzy models than that for regression modeI. The relative importance of yarn count and cotton fibre properties to rotor yarn elongation has also been studied. Yarn count and cotton fibre micronaire are found to be dominant input factors influencing the breaking elongation of rotor-spun yarns.
Page(s): 19-25
URI: http://hdl.handle.net/123456789/24599
ISSN: 0975-1025 (Online); 0971-0426 (Print)
Appears in Collections:IJFTR Vol.30(1) [March 2005]

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