期刊
JOURNAL OF THE TEXTILE INSTITUTE
卷 92, 期 1, 页码 157-163出版社
TEXTILE INST
DOI: 10.1080/00405000108659567
关键词
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A neural network computing technique was proposed to predict fabric end-use. One hundred samples of apparel fabrics were selected and measured using the Kawabata KES-FB instruments. Instrumental data of the fabric properties and information on fabric end-uses, suitings, shirts, and blouses, were input into a neural network software to train a multilayer perceptron model. The prediction error rate from the established neural network model was estimated by using a cross-validation method.
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