By Topic

The Research in Yarn Quality Prediction Model Based on an Improved BP Algorithm

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)
Fan Xiu-Juan ; Inf. Technol. Sch., Beijing Inst. of Fashion Technol., Beijing, China ; Li Cheng-Guo

This paper analyzes the defects and reasons for using standard BP neural network algorithm in building quality prediction model of yarns and explores an improved BP neural network algorithm. By increasing the back-propagation error-feedback signals and applying sell-adaptive and adjusting learning rate, the research has reinforced the adjustment of network weights and prevented network entering saturated region too early. These methods can increase the convergent speed of network and improve system stability. The experiment has proved that the forecast result is of high accuracy which comes from the improved BP neural network algorithm, and the design of quality prediction model is reasonable.

Published in:

Computer Science and Information Engineering, 2009 WRI World Congress on  (Volume:2 )

Date of Conference:

March 31 2009-April 2 2009