Proceedings of the 2016 International Conference on Mechatronics Engineering and Information Technology

Short Term Prediction of Coal Mine Methane Concentration with Chaos PSO-RBFNN Model in Underground Coal Mines

Authors
Yue Geng
Corresponding Author
Yue Geng
Available Online August 2016.
DOI
10.2991/icmeit-16.2016.7How to use a DOI?
Keywords
Coal mine gas concentration prediction, Chaotic analysis, PSO, RBFNN.
Abstract

Gas disaster is the serious threat to coal mine safety, the accurate prediction of coal mine methane (CMM) is one effective method avoiding the hazard occurrence. This paper first confirmed the chaotic characteristic of CMM sequence and calculated the delay time and embedding dimension. Combined chaotic sequential phase space reconstruction and the particle swarms optimized RBF neural network (PSO-RBFNN), build a new coupled model. The Chaotic reconstructed Time-series input PSO-RBF neural network model was proposed and highlighted its advantages by comparing the other three conventional models, Time-series input RBFNN (T-RBFNN), Chaotic reconstructed Time-series input RBFNN (CT-RBFNN) and PSO-RBFNN. The performance rank was CT-PSO-RBFNN, CT-RBFNN, T-PSO-RBFNN, T-RBFNN.

Copyright
© 2016, 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 2016 International Conference on Mechatronics Engineering and Information Technology
Series
Advances in Engineering Research
Publication Date
August 2016
ISBN
10.2991/icmeit-16.2016.7
ISSN
2352-5401
DOI
10.2991/icmeit-16.2016.7How to use a DOI?
Copyright
© 2016, 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  - Yue Geng
PY  - 2016/08
DA  - 2016/08
TI  - Short Term Prediction of Coal Mine Methane Concentration with Chaos PSO-RBFNN Model in Underground Coal Mines
BT  - Proceedings of the 2016 International Conference on Mechatronics Engineering and Information Technology
PB  - Atlantis Press
SP  - 36
EP  - 40
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmeit-16.2016.7
DO  - 10.2991/icmeit-16.2016.7
ID  - Geng2016/08
ER  -