Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)

The Population Prediction based on Grey Model and BP Neural Networks

Authors
Meng Liu, Yonglin Pang, Wei Xiang
Corresponding Author
Meng Liu
Available Online May 2017.
DOI
10.2991/icmeit-17.2017.58How to use a DOI?
Keywords
Population prediction; Grey model; BP neural network; Combination prediction model; MATLAB
Abstract

In this paper, the methods of population prediction are analyzed and studied. Considering the limitation of using the grey model and BP neural network, we established a new combination prediction model based on the two intelligent algorithms. First, the grey model and BP neural network were used to do data simulation on MATLAB. Then, a new combination forecasting model is obtained by using variance distribution. The result showed that the combination model can give full play to the advantages of single model as well as weaken its disadvantages and it is more ideal than the single model.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)
Series
Advances in Computer Science Research
Publication Date
May 2017
ISBN
10.2991/icmeit-17.2017.58
ISSN
2352-538X
DOI
10.2991/icmeit-17.2017.58How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Meng Liu
AU  - Yonglin Pang
AU  - Wei Xiang
PY  - 2017/05
DA  - 2017/05
TI  - The Population Prediction based on Grey Model and BP Neural Networks
BT  - Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)
PB  - Atlantis Press
SP  - 298
EP  - 302
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-17.2017.58
DO  - 10.2991/icmeit-17.2017.58
ID  - Liu2017/05
ER  -