Paper Title:
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.

  Info
Periodical
Chapter
Chapter 1: Textile Science and Technology
Edited by
Rui Wang and Huawu Liu
Pages
219-222
DOI
10.4028/www.scientific.net/AMR.331.219
Citation
H. J. Li, X. H. Wang, "Predicting Yarn Unevenness Using Improved BP Neural Network", Advanced Materials Research, Vol. 331, pp. 219-222, 2011
Online since
September 2011
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Ru Wang Yuan, Xiu Ming Jiang
Chapter 1: Material Science and Technology
Abstract:This paper compares capacitive and photoelectric method for measuring yarn evenness, and presents a new yarn unevenness on-line measurement...
60
Authors: Bo Zhao
Chapter 1: Material Engineering and its Application
Abstract:The artificial neural network and multiple regression models have been developed to predict the evenness of cotton ring yarn with process...
103
Authors: Jing Jin, Jiang Ping Wang
Chapter 4: Applied Materials: Study, Structure, and Technologies
Abstract:Yarn unevenness is one of the important indexes which presents the evenness of polyester POY filament and affects the performances of the...
460
Authors: Hui Jun Li, Xin Hou Wang
Chapter 2: Technology in Textile Industry
Abstract:The objective of this research is to predict yarn unevenness. The model of predicting yarn unevenness is built based on BP neural network....
329
Authors: Li Bin Lv, Mei Du
Chapter 4: Fundamental of Textile Science and Technology
Abstract:To research relation of card sliver quality and yarn quality, influence degree of card sliver parameters on yarn quality was analyzed through...
426