[직/편성물] Selecting Optimal Interlinings with a Neural Network 출판일 : 2000.11.01 저자 : Sung H. Jeong, Jung H. Kim, and Cheol J. Hong 서지사항 : Textile Research Journal, Volume 70, Issue 11, 1005페이지 등록일 : 2012.10.22 I 조회수 : 1016 작성자 : admin |
This paper reports on the construction of an integrated tool consisting of a neural network
and subjoined local approximation technique for application to the sewing process,
especially for selecting optimal interlinings for woolen fabrics. A single hidden layer neural
network is constructed with five input nodes, ten hidden nodes, and two output nodes. To
train the network with a back-propagation learning algorithm, the mechanical parameters
used as inputs for the fabrics are tensile energy, bending rigidity, bending hysteresis, shear
stiffness, and shear hysteresis, while mechanical parameters used as outputs for the
interlinings are bending rigidity and shear stiffness, all of which are measured on the KES-
FB system. Even though the back-propagation algorithm has a higher learning accuracy
and can be successfully used to select the appropriate interlining, its learning process is
too slow and it gets stuck in a local minimum. This research presents a few methods for
improving the efficiency of the learning process. The raw data from the KES-FB system are
nonlinearly normalized, and input orders are randomized. This proce dure produces a good
result, such that the error values of the prediction model are low despite the relatively small
data set for training. After training, the optimal interlinings for unknown fabrics can be
correctly selected through mapping and the local approximation method.
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