Please use this identifier to cite or link to this item:
Title: Comparative analysis of regression and ANN models for predicting drape coefficient of handloom fabrics
Authors: Mitra, Ashis
Majumdar, Abhijit
Majumdar, Prabal Kumar
Banerjee, Debamalya
Keywords: Artificial neural network;Drape coefficient;Handloom cotton fabric;Regression model
Issue Date: Dec-2012
Publisher: NISCAIR-CSIR, India
Abstract: This paper reports a comparative analysis of two modeling methodologies for the prediction of drape coefficient of handloom cotton fabrics. Four primary fabric constructional parameters, namely ends per inch, picks per inch, warp count, weft count and fabric areal density (g/m2) have been used as inputs for artificial neural network (ANN) and regression models. The prediction performance of both the models is found to be good as the correlation coefficient is higher than 0.9 and mean absolute error is less than 2.5%. However, ANN models are better than the regression models both in terms of correlation coefficient and mean absolute error. The importance of fabric parameters on drape coefficient has also been analysed by the developed ANN and regression models. The ranking of fabric parameters given by ANN and regression models are found to be in good agreement.
Page(s): 313-320
ISSN: 0975-1025 (Online); 0971-0426 (Print)
Appears in Collections:IJFTR Vol.37(4) [December 2012]

Files in This Item:
File Description SizeFormat 
IJFTR 37(4) 313-320.pdf119.38 kBAdobe PDFView/Open

Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.