Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

Prediction of Yarn Quality Based on Differential Evolutionary BP Neural Network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Jie Lv ; Dept. of Electr. & Inf. Eng., Ningxia Inst. of Sci. & Technol., Shizuishan, China ; Chenghui Cao

In order to improve the prediction accuracy of yarn quality based on BP neural network, in this paper, differential evolution algorithm is applied to train BP neural network. By using six parameters of raw cotton as the input node, and single yarn strength value and evenness CV value which characterize yarn quality indicators as the output node, a prediction model of yarn quality is developed. In the test of real data, it shows that the algorithm has a good effect, improves the prediction accuracy of the BP neural network algorithm and provides effective support for the prediction of yarn quality in enterprise.

Published in:

Computational and Information Sciences (ICCIS), 2012 Fourth International Conference on

Date of Conference:

17-19 Aug. 2012