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Z. Intelligent Animal Fiber Classification with Artificial Neural Networks. Textile Research Journal, 2002, 72(7), 594-600. S. S. Classifying Web Defects with a Back-Propagation Neural Network by Color Image Processing. Textile Research Journal, 2000, 70(7), 633-640. S. Y. New Approaches to Establishing Translation Equations for the Total Hand Value of Fabric. Textile Research Journal, 2004, 74(6), 528-534. , Özdil, N. and Taskin, C. The Effect of Fiber Properties on the Characteristics of Spliced Yarns: Part II: Prediction of Retained Spliced Diameter.

The results predicted by neural network were compared with the grades that classers judged (Kang & Kim, 2002). 3. Yarn, fabric, nonwoven and cloth defect detection and categorization In general, textile quality control is determined by measuring a large number of properties (including mechanical and physical properties, and etc), which in many cases can only be done by skilled workers or expensive equipments (Lien & Lee, 2002). Generally, In textile Artificial Neural Network Prosperities in Textile Applications 37 industry, textiles are inspected manually for defects, but some problems arise in this visual inspection, such as excessive time consumed, human subjective factors, stress on mind and body, and fatigue.

Finally they used an unsupervised neural network in which the outputs used as inputs in the supervised network (a multilayer perception with a back propagation algorithm) for classification while the fiber classes were the outputs of the output layer. 0005 within the first 100 epochs and three different numbers of units in the hidden layer (80, 50, and 20) was used. Multilayer perception used for fiber classification had a hyperbolic tangent activation function in the processing elements of the hidden layer and output layer.

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