Paper Title:
Fabric Sewability Prediction with Kernel PCA Based on SFC-RBFNN
  Abstract

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)
Chapter
Chapter 6: Measurement Technology and Instrument
Edited by
Xiaoming Qian and Huawu Liu
Pages
1143-1153
DOI
10.4028/www.scientific.net/AMR.332-334.1143
Citation
Y. H. Pan, F. Bao, M. G. Wu, "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
$32.00
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