DocumentCode
3006746
Title
Fabric Sewability Evaluation Based on KPCA Using SFC-RBFNN
Author
Pan, Yonghui
Author_Institution
Jiangyin Polytech. Coll., Jiangyin
fYear
2008
fDate
25-26 Sept. 2008
Firstpage
251
Lastpage
256
Abstract
In this paper, a supervised fuzzy clustering RBF neural network (SFC-RBFNN) based on kernel PCA is introduced for constructing the fabric sewability evaluation system. Our experimental results demonstrate that the proposed system could efficiently be used as an objective seam pucker evaluation system with high accuracy and is robust for various structures and mechanical properties of middle-thickness woolen fabric.
Keywords
fabrics; fuzzy set theory; principal component analysis; production engineering computing; radial basis function networks; RBF neural network; fabric sewability evaluation; kernel PCA; objective seam pucker evaluation system; supervised fuzzy clustering; Fabrics; Fuzzy neural networks; Fuzzy systems; Kernel; Mechanical factors; Neural networks; Principal component analysis; Stress; System testing; Yarn; RBF neural network; fabric sewability; kernel PCA; supervised fuzzy clustering;
fLanguage
English
Publisher
ieee
Conference_Titel
Genetic and Evolutionary Computing, 2008. WGEC '08. Second International Conference on
Conference_Location
Hubei
Print_ISBN
978-0-7695-3334-6
Type
conf
DOI
10.1109/WGEC.2008.52
Filename
4637438
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