Abstract
A novel prediction method combing a neural network with the D-S evidence theory for coal and gas outbursts is put forward in this paper. We take advantage of the fact that the non-linear input-output mapping function of the neural network can handle the non-linear parameters from coal and gas outburst monitor systems. And the output of the neural network is taken as the basic probability of the assignment function of the D-S evidence theory, which resolves the main problem of establishing the BPAF for the D-S evidence theory. The results from our experiments show that it is feasible and effective to combine the neural network with the D-S evidence theory for deciding on predictions. And using this method, we can make a more certain and credible prediction decision than witheach independent method.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Zhang, R.L.: Application of Advanced Information Technology on Coal and Gas Outburst Prediction. PhD thesis, Chongqing University (2004)
Li, C.W.: Research on Gay Classification and Prediction for Coal and Gas Outburst Quicksand. PhD thesis, China University of Mining and Technology (2005)
Hao, J.S.: Application of Improved BP Network in Prediction of Coal and Gas Outburst. Journal of Liaoning Technical University, 9–11 (2004)
Gao, L.F.: Prediction of Coal and Gas Outburst Disasters Based on Genetic and BP Algorithm. Journal of Liaoning Technical University, 408–410 (2002)
Zhang, T.G.: Prediction and Control of Coal and Gas Outburst in Pingdingshang Mining Area. Journal of China Coal Society, 173–177 (2001)
Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice-Hall, New Jersey (1999)
Su, H., Zheng, G.: A Non-Intrusive Drowsiness Related Accident Prediction Model Based on D-S Evidence Theory. In: Proceedings of the 1st International Conference on Bioinformatics and Biomedical Engineering (ICBBE 2007), Wuhan, China, pp. 570–573 (2007)
Regis, S., Desachy, J., Doncescu, A.: Evaluation of Biochemical Sources Pertinence in Classification of Cell’s Physiological States by Evidence Theory. In: Proceedings of 13th IEEE International Conference on Fuzzy Systems, Budapest, Hungary, pp. 867–872 (2004)
Tan, D., Yan, X., Gao, S., Liu, Z.: Fault Diagnosis for Spark Ignition Engine Based on Multi-sensor Data Fusion. In: Proceedings of IEEE International Conference on Vehicular Electronics and Safety, Xi’an, China, pp. 311–314 (2005)
Lu, W., Wu, Q., Shao, Q.: The D-S Evidence Theory and Application to Feasibility Evaluating of New Product Developing. Journal of Operational Research and Management, 111–115 (2004)
Cichocki, A., Unbehauen, R.: Neural Network for Optimization and Signal Processing. Wiley, Chichester (1993)
Duan, X.S.: Evidence Theory and Decision-making. Artificial Intelligent. China Rennin University Press, Beijing (1993)
Miao, Y.Z., Zhang, H.X., Zhang, J.W., Ma, X.P.: Improvement of the Combination Rules of the D-S Evidence Theory Based on Dealing with the Evidence Conflict. In: 4th IEEE International Conference on Information and Automation, Zhangjiajie, China (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Miao, Y., Zhang, J., Zhang, H., Ma, X., Zhao, Z. (2008). Coal and Gas Outburst Prediction Combining a Neural Network with the Dempster-Shafter Evidence. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_93
Download citation
DOI: https://doi.org/10.1007/978-3-540-87734-9_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87733-2
Online ISBN: 978-3-540-87734-9
eBook Packages: Computer ScienceComputer Science (R0)