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