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International Conference on Control Engineering and Mechanical Design (CEMD 2017)

Chao Li
Chao Li
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ASME Press
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Focusing on the nonlinearity of the relation between mechanical properties and the fabric drapability, a BP neural networks model as well as a support vector machine model are proposed to process the relationship mapping mechanical parameters to the fabric drape function and predicts the fabric drape coefficient. In the case of silk fabric, both the proposed SVM and the BP neural networks can realize excellent performance with respect to the drapability evaluation and prediction. Simulation experiments show that the SVM evaluation model outperforms the BP neural networks model when predicting the drape coefficient even though its approximation capability is less than BP neural networks model in the procedure of learning.

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