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Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007)
Prediction of R in Sinter Process based on Grey Neural Network Algebra
Haier International Training Center, Qingdao, China
July 30-August 01
ISBN: 0-7695-2909-7
ASCII Text
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Wang Ai-min, Song Qiang, "Prediction of R in Sinter Process based on Grey Neural Network Algebra," Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on, vol. 2, pp. 248-252, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007.
 
 
BibTex
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@article{ 10.1109/SNPD.2007.65,
author = {Wang Ai-min and Song Qiang},
title = {Prediction of R in Sinter Process based on Grey Neural Network Algebra},
journal ={Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on},
volume = {2},
year = {2007},
isbn = {0-7695-2909-7},
pages = {248-252},
doi = {http://doi.ieeecomputersociety.org/10.1109/SNPD.2007.65},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
TI - Prediction of R in Sinter Process based on Grey Neural Network Algebra
SN - 0-7695-2909-7
SP248
EP252
A1 - Wang Ai-min,
A1 - Song Qiang,
PY - 2007
KW - alkalinity of sinter
KW - grey neural network
KW - prediction
KW - the sintering process
KW - grey model.
VL - 2
JA - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
ER -
 
Wang Ai-min, Wuhan University of Technology, China; Anyang Normal University
Song Qiang, Anyang Institute of Technology
grey neural network model was proposed on the basis of the models.The fluctuation of data sequence is weakened by the grey theory and the neural network is capable of processing non-linear adaptable information, and the GNN is a combination of those advantages. The results reveal, the alkalinity of sinter can be accurately predicted through this model by reference to small sample and information. It was concluded that the GNN model is effective with the advantages of high precision, less requirement of samples and comparatively simple calculation.
Index Terms:
alkalinity of sinter, grey neural network, prediction, the sintering process, grey model.
Citation:
Wang Ai-min, Song Qiang, "Prediction of R in Sinter Process based on Grey Neural Network Algebra," snpd, vol. 2, pp.248-252, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007
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