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dc.contributor.authorMajumdar, Abhijit-
dc.contributor.authorMajumdar, Prabal Kumar-
dc.contributor.authorSarkar, Bijan-
dc.identifier.issn0975-1025 (Online); 0971-0426 (Print)-
dc.description.abstractArtificial neural network (ANN) models for predicting the single yarn tenacity of ring- and rotor- spun yarns form the cotton fibre properties, measured by high volume instrument, have been presented. Seven cotton fibre properties and yarn fineness have been used as the inputs to the neural network. Different network structures have been used to optimize the prediction performance. The relative importance of all the cotton fibre properties has also been quantified. The ANN models could predict the single yarn tenacity with less than 5% and 2% mean error in case of ring- and rotor- spun yarns respectively.Yarn fineness, fibre bundle tenacity, elongation and length uniformity are the dominant input parameters which influence the single yarn tenacity of spun yarns.en_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.relation.ispartofseriesInt. Cl.7 G06N 3/02; D02G 3/00en_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceIJFTR Vol.29(2) [June 2004]en_US
dc.subjectArtificial neural networken_US
dc.subjectBundle tenacityen_US
dc.subjectCotton fibreen_US
dc.subjectRing yarnen_US
dc.subjectRotor yarnen_US
dc.subjectYarn tenacityen_US
dc.titlePrediction of single yarn tenacity of ring-and rotor-spun yarns from HVI results using artificial neural networksen_US
Appears in Collections:IJFTR Vol.29(2) [June 2004]

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