Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

The Use of Neural Network BP Algorithm in Magnesium Smelting Process Parameter Optimization

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

3 Author(s)
Huiling Yuan ; Inst. of Mech. & Elec. Eng., Nanchang Univ., Nanchang, China ; Tianrui Zhou ; Jie Zhou

Because artificial neural networks discard the traditional modeling methods, it can extract domain knowledge from a large number of discrete experimental data via study and training, and express these knowledge as network connection weights, so as to establish the corresponding relation model. In this paper, based on neural network BP algorithm, we built a relation model that shows how various process parameters affect the magnesium output rate in Pidgeon magnesium reduction process. This laid a foundation for process parameters optimization.

Published in:

Computer Science and Information Engineering, 2009 WRI World Congress on  (Volume:5 )

Date of Conference:

March 31 2009-April 2 2009