Prediction Model of Sewing Technical Condition by Grey Neural Network  

作　　者：董英 方方 张渭源 

机构地区：[1]Fashion Institute,Donghua University

出　　处：《Journal of Donghua University(English Edition)》2007年第4期565-568,共4页东华大学学报（英文版）

摘　　要：The grey system theory and the artificial neural network technology were applied to predict the sewing technical condition. The representative parameters, such as needle, stitch, were selected. Prediction model was established based on the different fabrics’ mechanical properties that measured by KES instrument. Grey relevant degree analysis was applied to choose the input parameters of the neural network. The result showed that prediction model has good precision. The average relative error was 4.08% for needle and 4.25% for stitch.The grey system theory and the artificial neural network technology were applied to predict the sewing technical condition. The representative parameters, such as needle, stitch, were selected. Prediction model was established based on the different fabrics＇ mechanical properties that measured by KES instrument. Grey relevant degree analysis was applied to choose the input parameters of the neural network. The result showed that prediction model has good precision. The average relative error was 4.08% for needle and 4.25% for stitch.

关 键 词：grey relevant degree neural network NEEDLE STITCH KES measurement prediction model 

分 类 号：TS941.61[轻工技术与工程—服装设计与工程]