Title :
SFCNN Based Approach to Fabric Angle Bending Rigidity Prediction
Author_Institution :
Jiangyin Polytech. Coll., Jiangsu
Abstract :
In this paper, a supervised fuzzy clustering neural network (SFCNN) is introduced for constructing the fabric angle bending property prediction system. Our experimental results demonstrate that the proposed system could efficiently be used as a fabric angle bending rigidity prediction system with high accuracy and is robust for various structures and mechanical properties of cotton and wool fabric.
Keywords :
bending; cotton fabrics; fuzzy neural nets; pattern clustering; shear modulus; textile industry; wool; cotton fabric; fabric angle bending rigidity prediction; mechanical property; supervised fuzzy clustering neural network; wool fabric; Accuracy; Automatic testing; Clothing; Communication system control; Computer networks; Cotton; Fabrics; Materials testing; Strips; Wool; Angle bending rgidity; Fabric; Supervised FCNN; Supervised fuzzy clustering;
Conference_Titel :
Computing, Communication, Control, and Management, 2008. CCCM '08. ISECS International Colloquium on
Conference_Location :
Guangzhou
Print_ISBN :
978-0-7695-3290-5
DOI :
10.1109/CCCM.2008.278
