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Title: Prediction and optimization of yarn properties using genetic algorithm/artificial neural network
Authors: Subramanian, S N
Keywords: Air-jet nozzles
Artificial neural network
Back propagation network
Genetic algorithm
Hybrid technique
Jet ring-spun yarns
Issue Date: Dec-2007
Publisher: CSIR
Series/Report no.: Int. Cl.⁸ D02G3/00, G06N3/02
Abstract: Relative performance of the back propagation neural network (BPN) algorithm combined with genetic algorithm (GA) approach for the prediction/optimization of the properties of yarn produced on jet ring spinning system has been studied. Yarn samples of various linear densities have been produced on ring spinning machines using air-jet nozzles as retrofit by varying the nozzle parameter and the yarn properties studied. The hybrid application is used to predict selected yarn properties based on the effect of certain nozzle parameters. The network trained for a set of training vectors is found to predict the yarn properties for a compacting method with minimum error percentage. The proposed GA/BPN model could be extended to suggest a suitable compacting method for the desired yarn properties.
Description: 409-413
Appears in Collections:IJFTR Vol.32(4) [December 2007]

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