Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/10220
Title: Application of multiple linear regression and artificial neural network algorithms to predict the total hand value of summer knitted T-shirts
Authors: Hasani, Hossein
Shanbeh, Mohsen
Keywords: Artificial neural network;KES system;Multiple linear regression model;Total hand value;Weighted euclidean distance method
Issue Date: Sep-2010
Publisher: NISCAIR-CSIR, India
Abstract: A mathematical method, Weighted Euclidean Distance, has been applied for indirect determination of total hand value from the KES system parameters obtained for various summer knitted T-shirts. In this method, the weight of multivariable related to fabric hand has been determined from objective measurements without any resource to subjective evaluation. Artificial neural network with back propagation learning algorithm and multiple linear regression algorithm have been used to construct predictive models for the determination of total hand value of summer knitted T-shirts based on fabric mechanical properties measured on the KES system of each sample as input and total hand value predicted by mathematical model as desired output. The predictive power of optimized models is calculated and compared. The results reveal that the artificial neural network model is very effective for predicting the total hand value and has the better performance as compared to multiple linear regression model.
Page(s): 222-227
URI: http://hdl.handle.net/123456789/10220
ISSN: 0975-1025 (Online); 0971-0426 (Print)
Appears in Collections:IJFTR Vol.35(3) [September 2010]

Files in This Item:
File Description SizeFormat 
IJFTR 35(3) 222-227.pdf120.21 kBAdobe PDFView/Open


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