Predicting Yarn Unevenness Using Improved BP Neural Network

Hui Jun Li; Xin Hou Wang

doi:10.4028/www.scientific.net/AMR.331.219

Paper Titles

Weaving 3D Fabric with a “中” Shaped Cross-Section
p.202

Manufacture of Composite Nanofiber Mats with Fiber Diameters Grads by Electrospinning for Filtration Use
p.206

Study on the Tensile Properties of the High-Performance Cored Rope
p.210

The Application Study of the Fabric Reconstruction in the Design of Home Textiles
p.214

Predicting Yarn Unevenness Using Improved BP Neural Network
p.219

The Printing Technology of New Pigment Used on Cashmere Products
p.225

Research Progress of Fluoride-Containing Acrylate Derivatives and their Application on Textile
p.229

Effect of a Hydrophobic Finishing on Moisture Management Performance of Cotton Fabric
p.237

Combination Technology of Spunbond & Spunlace
p.241

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Predicting Yarn Unevenness Using Improved BP Neural Network

Abstract:

The objective of this research is to predict yarn unevenness. The model of predicting yarn unevenness is built based on improved BP neural network. The improved BP neural networks are trained with HVI test results of cotton and USTER TENSOJET 5-S400 test results of yarn. The results show prediction models based on improved BP neural network are very precise and efficient.