Abstract
A security situation prediction model is proposed to forecast security situation of rail station based on Empirical Mode Decomposition (EMD), Support Vector Machine (SVM), and Particle Swarm Optimization (PSO). First, the basic concepts of EMD, SVM, and PSO are introduced. Second, EMD is used to decompose the original security situations data into several IMFs. Then, PSO-SVM model is used to forecast each IMF in which PSO is used to optimize the parameters of SVM. Finally, the sum of each IMF’s forecasting result is the final prediction. The experimental results show the efficiency of the presented method in security situation prediction.
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Acknowledgments
This study was the Fundamental Research Funds for the Central Universities (AE89454), and the Science and Technology Program of Guangzhou (201508010010). The author gratefully acknowledges the anonymous reviewers for their careful work and thoughtful suggestions that have helped improve this paper substantially.
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Yao, X., Wang, F., Zhang, Y. (2016). A Prediction Model of Security Situation Based on EMD-PSO-SVM. In: Qin, Y., Jia, L., Feng, J., An, M., Diao, L. (eds) Proceedings of the 2015 International Conference on Electrical and Information Technologies for Rail Transportation. Lecture Notes in Electrical Engineering, vol 378. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49370-0_37
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DOI: https://doi.org/10.1007/978-3-662-49370-0_37
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