Fabric Sewability Prediction with Kernel PCA Based on SFC-RBFNN

Abstract:

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By extracting five kernel principal components of fabric FAST (Fabric Assurance by Simple Testing) low mechanical data, this paper proposed a supervised fuzzy clustering radial basis function neural network to construct fabric sewability prediction 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.

Info:

Periodical:

Advanced Materials Research (Volumes 332-334)

Edited by:

Xiaoming Qian and Huawu Liu

Pages:

1143-1153

DOI:

10.4028/www.scientific.net/AMR.332-334.1143

Citation:

Y. H. Pan et al., "Fabric Sewability Prediction with Kernel PCA Based on SFC-RBFNN", Advanced Materials Research, Vols. 332-334, pp. 1143-1153, 2011

Online since:

September 2011

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Price:

$35.00

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